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Econometrics is a branch of economics that utilizes statistical methods, mathematical models, and computational techniques to analyze and understand economic data. It combines economic theory, statistical analysis, and mathematical modeling to provide insights into economic phenomena and make predictions about future outcomes. In this essay, we will explore the key concepts and techniques in econometrics, its applications, and its significance in the field of economics. Econometrics is concerned with the development and application of statistical methods to analyze economic data. It aims to test economic theories, estimate economic relationships, and forecast future economic trends. The field of econometrics emerged in the early 20th century when economists realized the need to use statistical methods to analyze economic phenomena. The first step in econometric analysis is data collection. Economic data can be collected through various sources, such as surveys, administrative records, or publicly available datasets. Econometricians work with different types of data, including time series data, cross-sectional data, and panel data. Time series data captures observations of a variable over time, while cross-sectional data represents observations at a specific point in time. Panel data combines both time series and cross-sectional data by collecting observations on multiple entities over time. Econometric analysis relies on economic models that describe the relationships between economic variables. These models are based on certain assumptions about the behavior of individuals, firms, and markets. The most commonly used economic model in econometrics is the linear regression model, which assumes a linear relationship between the dependent variable and the independent variables. Linear regression is a fundamental technique in econometrics that estimates the relationship between a dependent variable and one or more independent variables. The estimated relationship is represented by an equation that specifies the effect of the independent variables on the dependent variable. The coefficients in the equation indicate the magnitude and direction of the relationship. Econometricians use various methods to estimate the coefficients, such as Ordinary Least Squares (OLS) estimation. Hypothesis testing is an essential part of econometric analysis. It involves formulating null and alternative hypotheses about the relationship between variables and testing these hypotheses using statistical tests. The most common hypothesis test in econometrics is the t-test, which assesses the significance of a coefficient in the regression model. The p-value obtained from the t-test helps determine whether the coefficient is statistically significant.  

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Time Series and Econometric Modelling: Advances in the Statistical Sciences: Festschrift in Honor of Professor V.M. Joshi’s 70th Birthday, Volume III Author: Ian B. MacNeill, Gary J. Umphrey, Richard A. L. Carter, A. Ian McLeod, Aman Ullah Published by Springer Netherlands ISBN: 978-94-010-8624-0 DOI: 10.1007/978-94-009-4790-0 Table of Contents: Approximation of Linear Systems Some Reflections on the Modelling of Time Series Model Selection and Forecasting: A Semi-Automatic Approach Smoothness in Regression: Asymptotic Considerations A Fast Graphical Goodness of Fit Test for Time Series Models Outliers in Time Series Predicting Demands in a Multi-Item Environment On the Efficiency of a Strongly Consistent Estimator in ARMA Models Recent Results for Time Series in M Dimensions Time Series Valued Experimental Designs: One-Way Analysis of Variance with Autocorrelated Errors Monthly versus Annual Revisions of Concurrent Seasonally Adjusted Series A Walsh-Fourier Approach to the Analysis of Binary Time Series Excitation of Geophysical Systems with Fractal Flicker Noise On Some ECF Procedures for Testing Independence Are Economic Variables Really Integrated of Order One? Fractional Matrix Calculus and the Distribution of Multivariate Tests On Robustness of Tests of Linear Restrictions in Regression Models with Elliptical Error Distributions Nonparametric Inference In Econometrics: New Applications Confidence Intervals for Ridge Regression Parameters Asymptotic Properties of Single Equation Errors in Variables Estimators in Rational Expectations Models

Time Series and Econometric Modelling: Advances in the Statistical Sciences: Festschrift in Honor of Professor V.M. Joshi’s 70th Birthday, Volume III Author: Ian B. MacNeill, Gary J. Umphrey, Richard A. L. Carter, A. Ian McLeod, Aman Ullah Published by Springer Netherlands ISBN: 978-94-010-8624-0 DOI: 10.1007/978-94-009-4790-0 Table of Contents: Approximation of Linear Systems Some Reflections on the Modelling of Time Series Model Selection and Forecasting: A Semi-Automatic Approach Smoothness in Regression: Asymptotic Considerations A Fast Graphical Goodness of Fit Test for Time Series Models Outliers in Time Series Predicting Demands in a Multi-Item Environment On the Efficiency of a Strongly Consistent Estimator in ARMA Models Recent Results for Time Series in M Dimensions Time Series Valued Experimental Designs: One-Way Analysis of Variance with Autocorrelated Errors Monthly versus Annual Revisions of Concurrent Seasonally Adjusted Series A Walsh-Fourier Approach to the Analysis of Binary Time Series Excitation of Geophysical Systems with Fractal Flicker Noise On Some ECF Procedures for Testing Independence Are Economic Variables Really Integrated of Order One? Fractional Matrix Calculus and the Distribution of Multivariate Tests On Robustness of Tests of Linear Restrictions in Regression Models with Elliptical Error Distributions Nonparametric Inference In Econometrics: New Applications Confidence Intervals for Ridge Regression Parameters Asymptotic Properties of Single Equation Errors in Variables Estimators in Rational Expectations Models

Time Series and Econometric Modelling: Advances in the Statistical Sciences: Festschrift in Honor of Professor V.M. Joshi’s 70th Birthday, Volume III Author: Ian B. MacNeill, Gary J. Umphrey, Richard A. L. Carter, A. Ian McLeod, Aman Ullah Published by Springer Netherlands ISBN: 978-94-010-8624-0 DOI: 10.1007/978-94-009-4790-0 Table of Contents: Approximation of Linear Systems Some Reflections on the Modelling of Time Series Model Selection and Forecasting: A Semi-Automatic Approach Smoothness in Regression: Asymptotic Considerations A Fast Graphical Goodness of Fit Test for Time Series Models Outliers in Time Series Predicting Demands in a Multi-Item Environment On the Efficiency of a Strongly Consistent Estimator in ARMA Models Recent Results for Time Series in M Dimensions Time Series Valued Experimental Designs: One-Way Analysis of Variance with Autocorrelated Errors Monthly versus Annual Revisions of Concurrent Seasonally Adjusted Series A Walsh-Fourier Approach to the Analysis of Binary Time Series Excitation of Geophysical Systems with Fractal Flicker Noise On Some ECF Procedures for Testing Independence Are Economic Variables Really Integrated of Order One? Fractional Matrix Calculus and the Distribution of Multivariate Tests On Robustness of Tests of Linear Restrictions in Regression Models with Elliptical Error Distributions Nonparametric Inference In Econometrics: New Applications Confidence Intervals for Ridge Regression Parameters Asymptotic Properties of Single Equation Errors in Variables Estimators in Rational Expectations Models

Time Series and Econometric Modelling: Advances in the Statistical Sciences: Festschrift in Honor of Professor V.M. Joshi’s 70th Birthday, Volume III Author: Ian B. MacNeill, Gary J. Umphrey, Richard A. L. Carter, A. Ian McLeod, Aman Ullah Published by Springer Netherlands ISBN: 978-94-010-8624-0 DOI: 10.1007/978-94-009-4790-0 Table of Contents: Approximation of Linear Systems Some Reflections on the Modelling of Time Series Model Selection and Forecasting: A Semi-Automatic Approach Smoothness in Regression: Asymptotic Considerations A Fast Graphical Goodness of Fit Test for Time Series Models Outliers in Time Series Predicting Demands in a Multi-Item Environment On the Efficiency of a Strongly Consistent Estimator in ARMA Models Recent Results for Time Series in M Dimensions Time Series Valued Experimental Designs: One-Way Analysis of Variance with Autocorrelated Errors Monthly versus Annual Revisions of Concurrent Seasonally Adjusted Series A Walsh-Fourier Approach to the Analysis of Binary Time Series Excitation of Geophysical Systems with Fractal Flicker Noise On Some ECF Procedures for Testing Independence Are Economic Variables Really Integrated of Order One? Fractional Matrix Calculus and the Distribution of Multivariate Tests On Robustness of Tests of Linear Restrictions in Regression Models with Elliptical Error Distributions Nonparametric Inference In Econometrics: New Applications Confidence Intervals for Ridge Regression Parameters Asymptotic Properties of Single Equation Errors in Variables Estimators in Rational Expectations Models

We propose a new modelling framework suitable for the description of atmospheric convective systems as a collection of distinct plumes. The literature contains many examples of models for collections of plumes in which strong simplifying assumptions are made, a diagnostic dependence of convection on the large-scale environment and the limit of many plumes often being imposed from the outset. Some recent studies have sought to remove one or the other of those assumptions. The proposed framework removes both, and is explicitly time-dependent and stochastic in its basic character. The statistical dynamics of the plume collection are defined through simple probabilistic rules applied at the level of individual plumes, and van Kampen's system size expansion is then used to construct the macroscopic limit of the microscopic model. Through suitable choices of the microscopic rules, the model is shown to encompass previous studies in the appropriate limits, and to allow their natural extensions beyond those limits.

This paper describes several applications in astronomy and cosmology that are addressed using probabilistic modelling and statistical inference.

This paper describes several applications in astronomy and cosmology that are addressed using probabilistic modelling and statistical inference.

This paper describes several applications in astronomy and cosmology that are addressed using probabilistic modelling and statistical inference.

Electric vehicles (EVs) add significant load on the power grid as they become widespread. The characteristics of this extra load follow the patterns of people's driving behaviours. In particular, random parameters such as arrival time and charging time of the vehicles determine their expected charging demand profile from the power grid. In this paper, we first present a model for uncoordinated charging power demand of EVs based on a stochastic process and accordingly we characterize an EV's expected daily power demand profile. Next, we illustrate it for different charging time distributions through simulations. This gives us useful insights into the long-term planning for upgrading power systems' infrastructure to accommodate EVs. Then, we incorporate departure time as another random variable into this modelling and introduce an autonomous demand response (DR) technique to manage the EVs' charging demand. Our results show that, it is possible to accommodate a large number of EVs and achieve the same peak-to-average ratio (PAR) in daily aggregated power consumption of the grid as when there is no EV in the system. This peak value can be decreased further significantly when we add vehicle-to-grid (V2G) capability in the system.

This article is from Malaria Journal , volume 13 . Abstract Background: Recurrent events data analysis is common in biomedicine. Literature review indicates that most statistical models used for such data are often based on time to the first event or consider events within a subject as independent. Even when taking into account the non-independence of recurrent events within subjects, data analyses are mostly done with continuous risk interval models, which may not be appropriate for treatments with sustained effects (e.g., drug treatments of malaria patients). Furthermore, results can be biased in cases of a confounding factor implying different risk exposure, e.g. in malaria transmission: if subjects are located at zones showing different environmental factors implying different risk exposures. Methods: This work aimed to compare four different approaches by analysing recurrent malaria episodes from a clinical trial assessing the effectiveness of three malaria treatments [artesunate + amodiaquine (AS + AQ), artesunate + sulphadoxine-pyrimethamine (AS + SP) or artemether-lumefantrine (AL)], with continuous and discontinuous risk intervals: Andersen-Gill counting process (AG-CP), Prentice-Williams-Peterson counting process (PWP-CP), a shared gamma frailty model, and Generalized Estimating Equations model (GEE) using Poisson distribution. Simulations were also made to analyse the impact of the addition of a confounding factor on malaria recurrent episodes. Results: Using the discontinuous interval analysis, AG-CP and Shared gamma frailty models provided similar estimations of treatment effect on malaria recurrent episodes when adjusted on age category. The patients had significant decreased risk of recurrent malaria episodes when treated with AS + AQ or AS + SP arms compared to AL arm; Relative Risks were: 0.75 (95% CI (Confidence Interval): 0.62-0.89), 0.74 (95% CI: 0.62-0.88) respectively for AG-CP model and 0.76 (95% CI: 0.64-0.89), 0.74 (95% CI: 0.62-0.87) for the Shared gamma frailty model.With both discontinuous and continuous risk intervals analysis, GEE Poisson distribution models failed to detect the effect of AS + AQ arm compared to AL arm when adjusted for age category. The discontinuous risk interval analysis was found to be the more appropriate approach. Conclusion: Repeated event in infectious diseases such as malaria can be analysed with appropriate existing models that account for the correlation between multiple events within subjects with common statistical software packages, after properly setting up the data structures.

This article is from Malaria Journal , volume 13 . Abstract Background: Recurrent events data analysis is common in biomedicine. Literature review indicates that most statistical models used for such data are often based on time to the first event or consider events within a subject as independent. Even when taking into account the non-independence of recurrent events within subjects, data analyses are mostly done with continuous risk interval models, which may not be appropriate for treatments with sustained effects (e.g., drug treatments of malaria patients). Furthermore, results can be biased in cases of a confounding factor implying different risk exposure, e.g. in malaria transmission: if subjects are located at zones showing different environmental factors implying different risk exposures. Methods: This work aimed to compare four different approaches by analysing recurrent malaria episodes from a clinical trial assessing the effectiveness of three malaria treatments [artesunate + amodiaquine (AS + AQ), artesunate + sulphadoxine-pyrimethamine (AS + SP) or artemether-lumefantrine (AL)], with continuous and discontinuous risk intervals: Andersen-Gill counting process (AG-CP), Prentice-Williams-Peterson counting process (PWP-CP), a shared gamma frailty model, and Generalized Estimating Equations model (GEE) using Poisson distribution. Simulations were also made to analyse the impact of the addition of a confounding factor on malaria recurrent episodes. Results: Using the discontinuous interval analysis, AG-CP and Shared gamma frailty models provided similar estimations of treatment effect on malaria recurrent episodes when adjusted on age category. The patients had significant decreased risk of recurrent malaria episodes when treated with AS + AQ or AS + SP arms compared to AL arm; Relative Risks were: 0.75 (95% CI (Confidence Interval): 0.62-0.89), 0.74 (95% CI: 0.62-0.88) respectively for AG-CP model and 0.76 (95% CI: 0.64-0.89), 0.74 (95% CI: 0.62-0.87) for the Shared gamma frailty model.With both discontinuous and continuous risk intervals analysis, GEE Poisson distribution models failed to detect the effect of AS + AQ arm compared to AL arm when adjusted for age category. The discontinuous risk interval analysis was found to be the more appropriate approach. Conclusion: Repeated event in infectious diseases such as malaria can be analysed with appropriate existing models that account for the correlation between multiple events within subjects with common statistical software packages, after properly setting up the data structures.

This article is from Malaria Journal , volume 13 . Abstract Background: Recurrent events data analysis is common in biomedicine. Literature review indicates that most statistical models used for such data are often based on time to the first event or consider events within a subject as independent. Even when taking into account the non-independence of recurrent events within subjects, data analyses are mostly done with continuous risk interval models, which may not be appropriate for treatments with sustained effects (e.g., drug treatments of malaria patients). Furthermore, results can be biased in cases of a confounding factor implying different risk exposure, e.g. in malaria transmission: if subjects are located at zones showing different environmental factors implying different risk exposures. Methods: This work aimed to compare four different approaches by analysing recurrent malaria episodes from a clinical trial assessing the effectiveness of three malaria treatments [artesunate + amodiaquine (AS + AQ), artesunate + sulphadoxine-pyrimethamine (AS + SP) or artemether-lumefantrine (AL)], with continuous and discontinuous risk intervals: Andersen-Gill counting process (AG-CP), Prentice-Williams-Peterson counting process (PWP-CP), a shared gamma frailty model, and Generalized Estimating Equations model (GEE) using Poisson distribution. Simulations were also made to analyse the impact of the addition of a confounding factor on malaria recurrent episodes. Results: Using the discontinuous interval analysis, AG-CP and Shared gamma frailty models provided similar estimations of treatment effect on malaria recurrent episodes when adjusted on age category. The patients had significant decreased risk of recurrent malaria episodes when treated with AS + AQ or AS + SP arms compared to AL arm; Relative Risks were: 0.75 (95% CI (Confidence Interval): 0.62-0.89), 0.74 (95% CI: 0.62-0.88) respectively for AG-CP model and 0.76 (95% CI: 0.64-0.89), 0.74 (95% CI: 0.62-0.87) for the Shared gamma frailty model.With both discontinuous and continuous risk intervals analysis, GEE Poisson distribution models failed to detect the effect of AS + AQ arm compared to AL arm when adjusted for age category. The discontinuous risk interval analysis was found to be the more appropriate approach. Conclusion: Repeated event in infectious diseases such as malaria can be analysed with appropriate existing models that account for the correlation between multiple events within subjects with common statistical software packages, after properly setting up the data structures.

A version of quantum theory is derived from a set of plausible assumptions related to the following general setting: For a given system there is a set of experiments that can be performed, and for each such experiment an ordinary statistical model is defined. The parameters of the single experiments are functions of a hyperparameter, which defines the state of the system. There is a symmetry group acting on the hyperparameters, and for the induced action on the parameters of the single experiment a simple consistency property is assumed, called permissibility of the parametric function. The other assumptions needed are rather weak. The derivation relies partly on quantum logic, partly on a group representation of the hyperparameter group, where the invariant spaces are shown to be in 1-1 correspondence with the equivalence classes of permissible parametric functions. Planck's constant only plays a role connected to generators of unitary group representations.

RooStatsCms is an object oriented statistical framework based on the RooFit technology. Its scope is to allow the modelling, statistical analysis and combination of multiple search channels for new phenomena in High Energy Physics. It provides a variety of methods described in literature implemented as classes, whose design is oriented to the execution of multiple CPU intensive jobs on batch systems or on the Grid.

We describe results from the third stage of a project to build a statistical model for hurricane tracks. In the first stage we modelled the unconditional mean track. In the second stage we modelled the unconditional variance of fluctuations around the mean. Now we address the question of how to model the autocorrelations in the standardised fluctuations. We perform a thorough diagnostic analysis of these fluctuations, and fit a type of AR(1) model. We then assess the goodness of fit of this model in a number of ways, including an out-of-sample comparison with a simpler model, an in-sample residual analysis, and a comparison of simulated tracks from the model with the observed tracks. Broadly speaking, the model captures the behaviour of observed hurricane tracks. In detail, however, there are a number of systematic errors.

Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst). Yet the nhst tests suffer from important shortcomings, which can be overcome by switching to Bayesian hypothesis testing. We propose a Bayesian hierarchical model which jointly analyzes the cross-validation results obtained by two classifiers on multiple data sets. It returns the posterior probability of the accuracies of the two classifiers being practically equivalent or significantly different. A further strength of the hierarchical model is that, by jointly analyzing the results obtained on all data sets, it reduces the estimation error compared to the usual approach of averaging the cross-validation results obtained on a given data set.

Usually one compares the accuracy of two competing classifiers via null hypothesis significance tests (nhst). Yet the nhst tests suffer from important shortcomings, which can be overcome by switching to Bayesian hypothesis testing. We propose a Bayesian hierarchical model which jointly analyzes the cross-validation results obtained by two classifiers on multiple data sets. It returns the posterior probability of the accuracies of the two classifiers being practically equivalent or significantly different. A further strength of the hierarchical model is that, by jointly analyzing the results obtained on all data sets, it reduces the estimation error compared to the usual approach of averaging the cross-validation results obtained on a given data set.

SciPy 2010 Day 2 Main Track 7 Statsmodels - Statistical Modelling in Python Skipper Seabold

In astrophysical (inverse) regression problems it is an important task to decide whether a given parametric model describes the observational data sufficiently well or whether a non-parametric modelling becomes necessary. However, in contrast to common practice this cannot be decided by solely comparing the quality of fit due to possible over-fitting by the non-parametric method. Therefore, in this paper we present a resampling algorithm which allows to decide whether deviations between a parametric and a non-parametric model are systematic or due to noise. The algorithm is based on a statistical comparison of the corresponding residuals, under the assumption of the parametric model as well as under violation of this assumption. This yields a graphical tool for a robust decision making of parametric versus non-parametric modelling. Moreover, our approach can be used for the selection of the most proper model among several competitors (model selection). The methods are illustrated by the problem of recovering the luminosity density in the Milky Way [MW] from near-infrared [NIR] surface brightness data of the DIRBE experiment on board of the COBE satellite. Among the parametric models investigated one with 4-armed spiral structure performs best. In this model the Sagittarius-Carina arm and its counter-arm are significantly weaker than the other pair of arms. Furthermore, we find statistical evidence for an improvement over a range of parametric models with different spiral structure morphologies by a non-parametric model of Bissantz & Gerhard (2002).

The behaviour of spoken dialogue systems is traditionally determined by expert-coded rules. A new set of rules is therefore required for each new domain. Also, rule-based systems do not naturally deal with uncertainty in the input, which makes them sensitive to speech understanding errors. Finally, such systems do not improve over time. The partially observable Markov decision process (POMDP) has been proposed as an alternative model for dialogue. POMDPs can automatically be optimised to maximise the reward, a measure of success. They are robust to speech understanding errors, and can adapt to new data. A POMDP-based dialogue manager maintains a distribution over every possible dialogue state, the belief state. Based on that distribution, the system chooses the action that gives the highest expected reward. The main challenge with the POMDP-based approach, however, is to make it tractable to maintain the belief state and optimise the action selection. In this talk a methods will be presented to make POMDP-based dialogue management scalable and suitable for flexible real-world dialogue systems. ©2011 Microsoft Corporation. All rights reserved.

ABS Research Paper: Exploration of state space modelling approaches for statistical impact measurment in ABS time series: The Labour Force Survey as a case study, Jun 2018

ABS Research Paper: Exploration of state space modelling approaches for statistical impact measurment in ABS time series: The Labour Force Survey as a case study, Jun 2018

We describe results from the second stage of a project to build a statistical model for hurricane tracks. In the first stage we modelled the unconditional mean track. We now attempt to model the unconditional variance of fluctuations around the mean. The variance models we describe use a semi-parametric nearest neighbours approach in which the optimal averaging length-scale is estimated using a jack-knife out-of-sample fitting procedure. We test three different models. These models consider the variance structure of the deviations from the unconditional mean track to be isotropic, anisotropic but uncorrelated, and anisotropic and correlated, respectively. The results show that, of these models, the anisotropic correlated model gives the best predictions of the distribution of future positions of hurricanes.

Research Master Thesis project by Iris van der Wulp at Utrecht University

We present a series of experiments focusing on the modelling of source-side context to improve Phrase-based Statistical Machine Translation. Statistical Machine Translation systems typically consist of a translation model and a language model. The former maps phrases in the source language to the target language, without regard for the context in which the source phrases occur. The latter models just the target language, and acts as a target-side model of context information after translation. We attempt to independently reproduce a line of existing research and test whether considering context information directly in the translation model has a positive effect on translation quality. We furthermore investigate various ways discriminative classifier-based models can be integrated into Statical Machine Translation. We will use proven techniques from Word Sense Disambiguation, effectively integrating these techniques in Statistical Machine Translation. Our approach is language-independent and knowledge-poor: we do not employ any explicit linguistic features computed by part-of-speech taggers, word sense disambiguation systems, supertaggers, or parsers, as used by previous work. We find only limited improvement of translation quality for certain formulaic corpora and conclude that explicit modelling of source-side context information does not add much to the data already implicitly available in the decode process.

We present a series of experiments focusing on the modelling of source-side context to improve Phrase-based Statistical Machine Translation. Statistical Machine Translation systems typically consist of a translation model and a language model. The former maps phrases in the source language to the target language, without regard for the context in which the source phrases occur. The latter models just the target language, and acts as a target-side model of context information after translation. We attempt to independently reproduce a line of existing research and test whether considering context information directly in the translation model has a positive effect on translation quality. We furthermore investigate various ways discriminative classifier-based models can be integrated into Statical Machine Translation. We will use proven techniques from Word Sense Disambiguation, effectively integrating these techniques in Statistical Machine Translation. Our approach is language-independent and knowledge-poor: we do not employ any explicit linguistic features computed by part-of-speech taggers, word sense disambiguation systems, supertaggers, or parsers, as used by previous work. We find only limited improvement of translation quality for certain formulaic corpora and conclude that explicit modelling of source-side context information does not add much to the data already implicitly available in the decode process.

We present a series of experiments focusing on the modelling of source-side context to improve Phrase-based Statistical Machine Translation. Statistical Machine Translation systems typically consist of a translation model and a language model. The former maps phrases in the source language to the target language, without regard for the context in which the source phrases occur. The latter models just the target language, and acts as a target-side model of context information after translation. We attempt to independently reproduce a line of existing research and test whether considering context information directly in the translation model has a positive effect on translation quality. We furthermore investigate various ways discriminative classifier-based models can be integrated into Statical Machine Translation. We will use proven techniques from Word Sense Disambiguation, effectively integrating these techniques in Statistical Machine Translation. Our approach is language-independent and knowledge-poor: we do not employ any explicit linguistic features computed by part-of-speech taggers, word sense disambiguation systems, supertaggers, or parsers, as used by previous work. We find only limited improvement of translation quality for certain formulaic corpora and conclude that explicit modelling of source-side context information does not add much to the data already implicitly available in the decode process.

We present a series of experiments focusing on the modelling of source-side context to improve Phrase-based Statistical Machine Translation. Statistical Machine Translation systems typically consist of a translation model and a language model. The former maps phrases in the source language to the target language, without regard for the context in which the source phrases occur. The latter models just the target language, and acts as a target-side model of context information after translation. We attempt to independently reproduce a line of existing research and test whether considering context information directly in the translation model has a positive effect on translation quality. We furthermore investigate various ways discriminative classifier-based models can be integrated into Statical Machine Translation. We will use proven techniques from Word Sense Disambiguation, effectively integrating these techniques in Statistical Machine Translation. Our approach is language-independent and knowledge-poor: we do not employ any explicit linguistic features computed by part-of-speech taggers, word sense disambiguation systems, supertaggers, or parsers, as used by previous work. We find only limited improvement of translation quality for certain formulaic corpora and conclude that explicit modelling of source-side context information does not add much to the data already implicitly available in the decode process.

We develop a Bayesian statistical model and estimation methodology based on Forward Projection Adaptive Markov chain Monte Carlo in order to perform the calibration of a high-dimensional non-linear system of Ordinary Differential Equations representing an epidemic model for Human Papillomavirus types 6 and 11 (HPV-6, HPV-11). The model is compartmental and involves stratification by age, gender and sexual activity-group. Developing this model and a means to calibrate it efficiently is relevant since HPV is a very multi-typed and common sexually transmitted infection with more than 100 types currently known. The two types studied in this paper, types 6 and 11, are causing about 90% of anogenital warts. We extend the development of a sexual mixing matrix for the population, based on a formulation first suggested by Garnett and Anderson. In particular we consider a stochastic mixing matrix framework which allows us to jointly estimate unknown attributes and parameters of the mixing matrix along with the parameters involved in the calibration of the HPV epidemic model. This matrix describes the sexual interactions between members of the population under study and relies on several quantities which are a-priori unknown. The Bayesian model developed allows one to estimate jointly the HPV-6 and HPV-11 epidemic model parameters such as the probability of transmission, HPV incubation period, duration of infection, duration of genital warts treatment, duration of immunity, the probability of seroconversion, per gender, age-group and sexual activity-group, as well as unknown sexual mixing matrix parameters related to assortativity. We conclude with simulation studies on synthetic and actual data from studies undertaken recently in Australia.

We develop a Bayesian statistical model and estimation methodology based on Forward Projection Adaptive Markov chain Monte Carlo in order to perform the calibration of a high-dimensional non-linear system of Ordinary Differential Equations representing an epidemic model for Human Papillomavirus types 6 and 11 (HPV-6, HPV-11). The model is compartmental and involves stratification by age, gender and sexual activity-group. Developing this model and a means to calibrate it efficiently is relevant since HPV is a very multi-typed and common sexually transmitted infection with more than 100 types currently known. The two types studied in this paper, types 6 and 11, are causing about 90% of anogenital warts. We extend the development of a sexual mixing matrix for the population, based on a formulation first suggested by Garnett and Anderson. In particular we consider a stochastic mixing matrix framework which allows us to jointly estimate unknown attributes and parameters of the mixing matrix along with the parameters involved in the calibration of the HPV epidemic model. This matrix describes the sexual interactions between members of the population under study and relies on several quantities which are a-priori unknown. The Bayesian model developed allows one to estimate jointly the HPV-6 and HPV-11 epidemic model parameters such as the probability of transmission, HPV incubation period, duration of infection, duration of genital warts treatment, duration of immunity, the probability of seroconversion, per gender, age-group and sexual activity-group, as well as unknown sexual mixing matrix parameters related to assortativity. We conclude with simulation studies on synthetic and actual data from studies undertaken recently in Australia.

We develop a Bayesian statistical model and estimation methodology based on Forward Projection Adaptive Markov chain Monte Carlo in order to perform the calibration of a high-dimensional non-linear system of Ordinary Differential Equations representing an epidemic model for Human Papillomavirus types 6 and 11 (HPV-6, HPV-11). The model is compartmental and involves stratification by age, gender and sexual activity-group. Developing this model and a means to calibrate it efficiently is relevant since HPV is a very multi-typed and common sexually transmitted infection with more than 100 types currently known. The two types studied in this paper, types 6 and 11, are causing about 90% of anogenital warts. We extend the development of a sexual mixing matrix for the population, based on a formulation first suggested by Garnett and Anderson. In particular we consider a stochastic mixing matrix framework which allows us to jointly estimate unknown attributes and parameters of the mixing matrix along with the parameters involved in the calibration of the HPV epidemic model. This matrix describes the sexual interactions between members of the population under study and relies on several quantities which are a-priori unknown. The Bayesian model developed allows one to estimate jointly the HPV-6 and HPV-11 epidemic model parameters such as the probability of transmission, HPV incubation period, duration of infection, duration of genital warts treatment, duration of immunity, the probability of seroconversion, per gender, age-group and sexual activity-group, as well as unknown sexual mixing matrix parameters related to assortativity. We conclude with simulation studies on synthetic and actual data from studies undertaken recently in Australia.

We present a probabilistic model for constraint-based grammars and a method for estimating the parameters of such models from incomplete, i.e., unparsed data. Whereas methods exist to estimate the parameters of probabilistic context-free grammars from incomplete data (Baum 1970), so far for probabilistic grammars involving context-dependencies only parameter estimation techniques from complete, i.e., fully parsed data have been presented (Abney 1997). However, complete-data estimation requires labor-intensive, error-prone, and grammar-specific hand-annotating of large language corpora. We present a log-linear probability model for constraint logic programming, and a general algorithm to estimate the parameters of such models from incomplete data by extending the estimation algorithm of Della-Pietra, Della-Pietra, and Lafferty (1997) to incomplete data settings.

This paper proposes a new channel modelling method for powerline communications networks based on the multipath profile in the time domain. The new channel model is developed to be applied in a range of Powerline Communications (PLC) research topics such as impulse noise modelling, deployment and coverage studies, and communications theory analysis. To develop the methodology, channels are categorised according to their propagation distance and power delay profile. The statistical multipath parameters such as path arrival time, magnitude and interval for each category are analyzed to build the model. Each generated channel based on the proposed statistical model represents a different realisation of a PLC network. Simulation results in similar the time and frequency domains show that the proposed statistical modelling method, which integrates the impact of network topology presents the PLC channel features as the underlying transmission line theory model. Furthermore, two potential application scenarios are described to show the channel model is applicable to capacity analysis and correlated impulse noise modelling for PLC networks.

The last glacial period was punctuated by a series of abrupt climate shifts, the so-called Dansgaard-Oeschger (DO) events. The frequency of DO events varied in time, supposedly because of changes in background climate conditions. Here, the influence of external forcings on DO events is investigated with statistical modelling. We assume two types of simple stochastic dynamical systems models (double-well potential-type and oscillator-type), forced by the northern hemisphere summer insolation change and/or the global ice volume change. The model parameters are estimated by using the maximum likelihood method with the NGRIP Ca$^{2+}$ record. The stochastic oscillator model with at least the ice volume forcing reproduces well the sample autocorrelation function of the record and the frequency changes of warming transitions in the last glacial period across MISs 2, 3, and 4. The model performance is improved with the additional insolation forcing. The BIC scores also suggest that the ice volume forcing is relatively more important than the insolation forcing, though the strength of evidence depends on the model assumption. Finally, we simulate the average number of warming transitions in the past four glacial periods, assuming the model can be extended beyond the last glacial, and compare the result with an Iberian margin sea-surface temperature (SST) record (Martrat et al., Science, vol. 317, p. 502, 2007). The simulation result supports the previous observation that abrupt millennial-scale climate changes in the penultimate glacial (MIS 6) are less frequent than in the last glacial (MISs 2-4). On the other hand, it suggests that the number of abrupt millennial-scale climate changes in older glacial periods (MISs 6, 8, and 10) might be larger than inferred from the SST record.

We discuss how ensemble weather forecasts can be used, and highlight the advantages and disadvantages of two particular methods.

We discuss how ensemble weather forecasts can be used, and highlight the advantages and disadvantages of two particular methods.

We discuss how ensemble weather forecasts can be used, and highlight the advantages and disadvantages of two particular methods.

This article is from PLoS ONE , volume 9 . Abstract The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.

This article is from PLoS ONE , volume 9 . Abstract The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.

This article is from PLoS ONE , volume 9 . Abstract The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.

This article is from PLoS ONE , volume 9 . Abstract The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.

In this dissertation, an abstract formalism extending information geometry is introduced. This framework encompasses a broad range of modelling problems, including possible applications in machine learning and in the information theoretical foundations of quantum theory. Its purely geometrical foundations make no use of probability theory and very little assumptions about the data or the models are made. Starting only from a divergence function, a Riemannian geometrical structure consisting of a metric tensor and an affine connection is constructed and its properties are investigated. Also the relation to information geometry and in particular the geometry of exponential families of probability distributions is elucidated. It turns out this geometrical framework offers a straightforward way to determine whether or not a parametrised family of distributions can be written in exponential form. Apart from the main theoretical chapter, the dissertation also contains a chapter of examples illustrating the application of the formalism and its geometric properties, a brief introduction to differential geometry and a historical overview of the development of information geometry.

In this dissertation, an abstract formalism extending information geometry is introduced. This framework encompasses a broad range of modelling problems, including possible applications in machine learning and in the information theoretical foundations of quantum theory. Its purely geometrical foundations make no use of probability theory and very little assumptions about the data or the models are made. Starting only from a divergence function, a Riemannian geometrical structure consisting of a metric tensor and an affine connection is constructed and its properties are investigated. Also the relation to information geometry and in particular the geometry of exponential families of probability distributions is elucidated. It turns out this geometrical framework offers a straightforward way to determine whether or not a parametrised family of distributions can be written in exponential form. Apart from the main theoretical chapter, the dissertation also contains a chapter of examples illustrating the application of the formalism and its geometric properties, a brief introduction to differential geometry and a historical overview of the development of information geometry.

This short technical paper advocates a bootstrapping algorithm from which we can form a statistically reliable opinion based on limited clinically observed data, regarding whether an osteo-hyperplasia could actually be a case of Ewing’s osteosarcoma. The basic premise underlying our methodology is that a primary bone tumour, if it is indeed Ewing’s osteosarcoma, cannot increase in volume beyond some critical limit without showing metastasis. We propose a statistical method to extrapolate such critical limit to primary tumour volume. Our model does not involve any physiological variables but rather is entirely based on time series observations of increase in primary tumour volume from the point of initial detection to the actual detection of metastases.

This short technical paper advocates a bootstrapping algorithm from which we can form a statistically reliable opinion based on limited clinically observed data, regarding whether an osteo-hyperplasia could actually be a case of Ewing’s osteosarcoma. The basic premise underlying our methodology is that a primary bone tumour, if it is indeed Ewing’s osteosarcoma, cannot increase in volume beyond some critical limit without showing metastasis. We propose a statistical method to extrapolate such critical limit to primary tumour volume. Our model does not involve any physiological variables but rather is entirely based on time series observations of increase in primary tumour volume from the point of initial detection to the actual detection of metastases.

We are building a hurricane number prediction scheme that relies, in part, on statistical modelling of the empirical relationship between Atlantic sea surface temperatures and landfalling hurricane numbers. We test out a number of simple statistical models for that relationship, using data from 1900 to 2005 and data from 1950 to 2005, and for both all hurricane numbers and intense hurricane numbers. The results are very different from the corresponding analysis for basin hurricane numbers.

We are building a hurricane number prediction scheme that relies, in part, on statistical modelling of the empirical relationship between Atlantic sea surface temperatures and landfalling hurricane numbers. We test out a number of simple statistical models for that relationship, using data from 1900 to 2005 and data from 1950 to 2005, and for both all hurricane numbers and intense hurricane numbers. The results are very different from the corresponding analysis for basin hurricane numbers.

This article is from Trials , volume 14 . Abstract None