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Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite-scores is preferred for prediction and diagnosis of individuals. While path analysis with composite-scores has been criticized for yielding biased parameter estimates, recent literature pointed out that the population values of parameters in a latent-variable model depend on artificially assigned scales. Consequently, bias in parameter estimates is not a well-grounded concept for models involving latent constructs. This article compares path analysis with composite-scores against SEM with respect to effect size and statistical power in testing the significance of the path coefficients, via the z- or t-statistics. The data come from many sources with various models that are substantively determined. Results show that SEM is not as powerful as path analysis even with equally-weighted-composites. But path analysis with Bartlett-factor- scores and the partial-least-squares approach to SEM perform the best with respect to effect size and power. [This paper will be published in "Behavior Research Methods." Discrepancy between the title of the article and authored paper.]

Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite-scores is preferred for prediction and diagnosis of individuals. While path analysis with composite-scores has been criticized for yielding biased parameter estimates, recent literature pointed out that the population values of parameters in a latent-variable model depend on artificially assigned scales. Consequently, bias in parameter estimates is not a well-grounded concept for models involving latent constructs. This article compares path analysis with composite-scores against SEM with respect to effect size and statistical power in testing the significance of the path coefficients, via the z- or t-statistics. The data come from many sources with various models that are substantively determined. Results show that SEM is not as powerful as path analysis even with equally-weighted-composites. But path analysis with Bartlett-factor- scores and the partial-least-squares approach to SEM perform the best with respect to effect size and power. [This paper will be published in "Behavior Research Methods." Discrepancy between the title of the article and authored paper.]

Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite-scores is preferred for prediction and diagnosis of individuals. While path analysis with composite-scores has been criticized for yielding biased parameter estimates, recent literature pointed out that the population values of parameters in a latent-variable model depend on artificially assigned scales. Consequently, bias in parameter estimates is not a well-grounded concept for models involving latent constructs. This article compares path analysis with composite-scores against SEM with respect to effect size and statistical power in testing the significance of the path coefficients, via the z- or t-statistics. The data come from many sources with various models that are substantively determined. Results show that SEM is not as powerful as path analysis even with equally-weighted-composites. But path analysis with Bartlett-factor- scores and the partial-least-squares approach to SEM perform the best with respect to effect size and power. [This paper will be published in "Behavior Research Methods." Discrepancy between the title of the article and authored paper.]

Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite-scores is preferred for prediction and diagnosis of individuals. While path analysis with composite-scores has been criticized for yielding biased parameter estimates, recent literature pointed out that the population values of parameters in a latent-variable model depend on artificially assigned scales. Consequently, bias in parameter estimates is not a well-grounded concept for models involving latent constructs. This article compares path analysis with composite-scores against SEM with respect to effect size and statistical power in testing the significance of the path coefficients, via the z- or t-statistics. The data come from many sources with various models that are substantively determined. Results show that SEM is not as powerful as path analysis even with equally-weighted-composites. But path analysis with Bartlett-factor- scores and the partial-least-squares approach to SEM perform the best with respect to effect size and power. [This paper will be published in "Behavior Research Methods." Discrepancy between the title of the article and authored paper.]

Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite-scores is preferred for prediction and diagnosis of individuals. While path analysis with composite-scores has been criticized for yielding biased parameter estimates, recent literature pointed out that the population values of parameters in a latent-variable model depend on artificially assigned scales. Consequently, bias in parameter estimates is not a well-grounded concept for models involving latent constructs. This article compares path analysis with composite-scores against SEM with respect to effect size and statistical power in testing the significance of the path coefficients, via the z- or t-statistics. The data come from many sources with various models that are substantively determined. Results show that SEM is not as powerful as path analysis even with equally-weighted-composites. But path analysis with Bartlett-factor- scores and the partial-least-squares approach to SEM perform the best with respect to effect size and power. [This paper will be published in "Behavior Research Methods." Discrepancy between the title of the article and authored paper.]

Structural equation modeling (SEM) has been deemed as a proper method when variables contain measurement errors. In contrast, path analysis with composite-scores is preferred for prediction and diagnosis of individuals. While path analysis with composite-scores has been criticized for yielding biased parameter estimates, recent literature pointed out that the population values of parameters in a latent-variable model depend on artificially assigned scales. Consequently, bias in parameter estimates is not a well-grounded concept for models involving latent constructs. This article compares path analysis with composite-scores against SEM with respect to effect size and statistical power in testing the significance of the path coefficients, via the z- or t-statistics. The data come from many sources with various models that are substantively determined. Results show that SEM is not as powerful as path analysis even with equally-weighted-composites. But path analysis with Bartlett-factor- scores and the partial-least-squares approach to SEM perform the best with respect to effect size and power. [This paper will be published in "Behavior Research Methods." Discrepancy between the title of the article and authored paper.]

Temperature control plays a crucial role in various industrial processes, ensuring optimal performance and product quality. The conventional approach to optimizing temperature controller parameters involves manual tuning, which can be time-consuming, labor-intensive, and often lacks precision. This paper introduces an innovative methodology for optimizing the parameters of a temperature controller by integrating statistical methods in the preparation of the experimental plan utilized by neural networks. The integration of statistical techniques in designing the experimental framework enhances the efficiency of data collection, providing a robust foundation for subsequent analysis. The neural network leverages this well-structured dataset to model and optimize the temperature controller parameters, resulting in improved precision and performance. The synergistic integration of statistical methods and neural networks not only streamlines the optimization process but also enhances the reliability of the temperature control system. The effectiveness of the proposed approach is demonstrated through case studies on the Procon level/flow and temperature 38-003 process. The results show significant improvements in temperature control performance, with reduced process variability and faster response times.

Temperature control plays a crucial role in various industrial processes, ensuring optimal performance and product quality. The conventional approach to optimizing temperature controller parameters involves manual tuning, which can be time-consuming, labor-intensive, and often lacks precision. This paper introduces an innovative methodology for optimizing the parameters of a temperature controller by integrating statistical methods in the preparation of the experimental plan utilized by neural networks. The integration of statistical techniques in designing the experimental framework enhances the efficiency of data collection, providing a robust foundation for subsequent analysis. The neural network leverages this well-structured dataset to model and optimize the temperature controller parameters, resulting in improved precision and performance. The synergistic integration of statistical methods and neural networks not only streamlines the optimization process but also enhances the reliability of the temperature control system. The effectiveness of the proposed approach is demonstrated through case studies on the Procon level/flow and temperature 38-003 process. The results show significant improvements in temperature control performance, with reduced process variability and faster response times.

Obesity rates have been increasing over recent decades, causing significant concern among policy makers. Excess body fat, commonly measured by body mass index (BMI), is a major risk factor for several common disorders including diabetes and cardiovascular disease, placing a substantial burden on health care systems. % Body mass index (BMI) is one indicator for excess body fat. To guide effective public health action, we need to understand the complex system of intercorrelated influences on BMI. This paper will review both classical and modern statistical methods for BMI analysis, highlighting that most of the classical methods are simple and easy to implement but ignore the complexity of data and structure, whereas modern methods do take complexity into consideration but can be difficult to implement. A series of case studies are presented to illustrate these methods and some potentially useful new models are suggested.

Source: Digital Library of India Scanning Centre: C-DAC, Noida Source Library: Delhi Engineering College Date Accessioned: 7/10/2015 22:09 The Digital Library of India was a project under the auspices of the Government of India.

The utilization of statistical methods an their applications within the new field of study known as Topological Data Analysis has has tremendous potential for broadening our exploration and understanding of complex, high-dimensional data spaces. This paper provides an introductory overview of the mathematical underpinnings of Topological Data Analysis, the workflow to convert samples of data to topological summary statistics, and some of the statistical methods developed for performing inference on these topological summary statistics. The intention of this non-technical overview is to motivate statisticians who are interested in learning more about the subject.

The utilization of statistical methods an their applications within the new field of study known as Topological Data Analysis has has tremendous potential for broadening our exploration and understanding of complex, high-dimensional data spaces. This paper provides an introductory overview of the mathematical underpinnings of Topological Data Analysis, the workflow to convert samples of data to topological summary statistics, and some of the statistical methods developed for performing inference on these topological summary statistics. The intention of this non-technical overview is to motivate statisticians who are interested in learning more about the subject.

The utilization of statistical methods an their applications within the new field of study known as Topological Data Analysis has has tremendous potential for broadening our exploration and understanding of complex, high-dimensional data spaces. This paper provides an introductory overview of the mathematical underpinnings of Topological Data Analysis, the workflow to convert samples of data to topological summary statistics, and some of the statistical methods developed for performing inference on these topological summary statistics. The intention of this non-technical overview is to motivate statisticians who are interested in learning more about the subject.

Previous editions published under title: Statistical methods applied to economics and business

(Abridged) This is the first of a series of papers in which we derive simultaneous constraints on cosmological parameters and X-ray scaling relations using observations of the growth of massive, X-ray flux-selected galaxy clusters. Our data set consists of 238 clusters drawn from the ROSAT All-Sky Survey, and incorporates extensive follow-up observations using the Chandra X-ray Observatory. Here we describe and implement a new statistical framework required to self-consistently produce simultaneous constraints on cosmology and scaling relations from such data, and present results on models of dark energy. In spatially flat models with a constant dark energy equation of state, w, the cluster data yield Omega_m=0.23 +- 0.04, sigma_8=0.82 +- 0.05, and w=-1.01 +- 0.20, marginalizing over conservative allowances for systematic uncertainties. These constraints agree well and are competitive with independent data in the form of cosmic microwave background (CMB) anisotropies, type Ia supernovae (SNIa), cluster gas mass fractions (fgas), baryon acoustic oscillations (BAO), galaxy redshift surveys, and cosmic shear. The combination of our data with current CMB, SNIa, fgas, and BAO data yields Omega_m=0.27 +- 0.02, sigma_8=0.79 +- 0.03, and w=-0.96 +- 0.06 for flat, constant w models. For evolving w models, marginalizing over transition redshifts in the range 0.05-1, we constrain the equation of state at late and early times to be respectively w_0=-0.88 +- 0.21 and w_et=-1.05 +0.20 -0.36. The combined data provide constraints equivalent to a DETF FoM of 15.5. Our results highlight the power of X-ray studies to constrain cosmology. However, the new statistical framework we apply to this task is equally applicable to cluster studies at other wavelengths.

The estimation of cosmological parameters from precision observables is an important industry with crucial ramifications for particle physics. This article discusses the statistical methods presently used in cosmological data analysis, highlighting the main assumptions and uncertainties. The topics covered are parameter estimation, model selection, multi-model inference, and experimental design, all primarily from a Bayesian perspective.

The estimation of cosmological parameters from precision observables is an important industry with crucial ramifications for particle physics. This article discusses the statistical methods presently used in cosmological data analysis, highlighting the main assumptions and uncertainties. The topics covered are parameter estimation, model selection, multi-model inference, and experimental design, all primarily from a Bayesian perspective.

Several methods of statistical analysis are proposed and analyzed in application for a specific task -- extraction of the structure functions from the cross sections of deep inelastic interactions of any type. We formulate the method based on the orthogonal weight functions and on an optimization procedure of errors minimization as well as methods underlying common $\chi^2$ minimization. Effectiveness of these methods usage is analyzed by comparison of the statistical parameters such as bias, extraction variance etc., for sample deep inelastic scattering data set.

For there to be a successful measurement of the 21 cm Epoch of Reionization (EoR) power spectrum, it is crucial that strong foreground contaminants be robustly suppressed. These foregrounds come from a variety of sources (such as Galactic synchrotron emission and extragalactic point sources), but almost all share the property of being spectrally smooth, and when viewed through the chromatic response of an interferometer, occupy a signature "wedge" region in cylindrical $k_\perp k_\parallel$ Fourier space. The complement of the foreground wedge is termed the "EoR window", and is expected to be mostly foreground-free, allowing clean measurements of the power spectrum. This paper is a sequel to a previous paper that established a rigorous mathematical framework for describing the foreground wedge and the EoR window. Here, we use our framework to explore statistical methods by which the EoR window can be enlarged, thereby increasing the sensitivity of a power spectrum measurement. We adapt the FKP approximation (commonly used in galaxy surveys) for 21 cm cosmology, and also compare the optimal quadratic estimator to simpler estimators that ignore covariances between different Fourier modes. The optimal quadratic estimator is found to suppress foregrounds by an extra factor of $\sim 10^5$ in power at the peripheries of the EoR window, boosting the detection of the cosmological signal from $12\sigma$ to $50\sigma$ at the midpoint of reionization in our fiducial models. If numerical issues can be finessed, decorrelation techniques allow the EoR window to be further enlarged, enabling measurements to be made deep within the foreground wedge. These techniques do not assume that foreground are Gaussian-distributed, and we additionally prove that a final round of foreground subtraction can be performed after decorrelation in a way that is guaranteed to have no cosmological signal loss.

The collection described the numerical methods of spectral and correlative analysis of stationary random processes. An expression for the numerical estimation of these characteristics was derived and analyzed. The results of the analysis of model processes of known statistical characteristics were realized on a computer, and the real experimental processes were represented as an extensive graphical information. The practical applicability of the numerical methods was the main purpose of the collection.

Book Source: Digital Library of India Item 2015.223124 dc.contributor.author: Goulden,c.h. dc.date.accessioned: 2015-07-09T23:05:45Z dc.date.available: 2015-07-09T23:05:45Z dc.date.digitalpublicationdate: 2005-05-23 dc.date.citation: 1939 dc.identifier.barcode: 2990140054002 dc.identifier.origpath: /data_copy/upload/0054/007 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/223124 dc.description.scannerno: 7 dc.description.scanningcentre: Osmania University dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 296 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: John Wiley And Sons Inc. dc.rights: Copyright Permitted dc.source.library: Osmania University dc.subject.classification: Natural Sciences dc.subject.classification: Mathematics dc.subject.classification: Analysis dc.title: Methods Of Statistical Analysis

Book Source: Digital Library of India Item 2015.223124 dc.contributor.author: Goulden,c.h. dc.date.accessioned: 2015-07-09T23:05:45Z dc.date.available: 2015-07-09T23:05:45Z dc.date.digitalpublicationdate: 2005-05-23 dc.date.citation: 1939 dc.identifier.barcode: 2990140054002 dc.identifier.origpath: /data_copy/upload/0054/007 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/223124 dc.description.scannerno: 7 dc.description.scanningcentre: Osmania University dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 296 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: John Wiley And Sons Inc. dc.rights: Copyright Permitted dc.source.library: Osmania University dc.subject.classification: Natural Sciences dc.subject.classification: Mathematics dc.subject.classification: Analysis dc.title: Methods Of Statistical Analysis

Book Source: Digital Library of India Item 2015.113142 dc.contributor.author: Rhodes, E.c. dc.date.accessioned: 2015-07-02T14:08:39Z dc.date.available: 2015-07-02T14:08:39Z dc.date.digitalpublicationdate: 2012-06-00 dc.date.citation: 1948 dc.identifier.barcode: 99999990302384 dc.identifier.origpath: /data9/upload/0300/316 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/113142 dc.description.scanningcentre: Banasthali University dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 247 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: London, George Routledge Amp Sons Ltd. dc.rights: Out_of_copyright dc.source.library: Government College, Kota dc.subject.classification: Economics dc.title: Elementary Statistical Methods

In quantum physics, all measured observables are subject to statistical uncertainties, which arise from the quantum nature as well as the experimental technique. We consider the statistical uncertainty of the so-called sampling method, in which one estimates the expectation value of a given observable by empirical means of suitable pattern functions. We show that if the observable can be written as a function of a single directly measurable operator, the variance of the estimate from the sampling method equals to the quantum mechanical one. In this sense, we say that the estimate is on the quantum mechanical level of uncertainty. In contrast, if the observable depends on non-commuting operators, e.g. different quadratures, the quantum mechanical level of uncertainty is not achieved. The impact of the results on quantum tomography is discussed, and different approaches to quantum tomographic measurements are compared. It is shown explicitly for the estimation of quasiprobabilities of a quantum state, that balanced homodyne tomography does not operate on the quantum mechanical level of uncertainty, while the unbalanced homodyne detection does.

Macroeconomic indicator forecasting is a difficult task and the macroeconomy’s complex operations and dynamic nature make it even more difficult. Machine Learning and Deep Learning methodologies have been investigated as alternatives to traditional forecasting methods because of recent developments in computing power and the emergence of data. How the Machine Learning and Deep Learning paradigms apply to a variety of Macro datasets have been examined in this research paper. Few Machine Learning and Deep Learning algorithms have been trained and their forecasting accuracy has been compared with that of traditional statistical method ARIMA.

Macroeconomic indicator forecasting is a difficult task and the macroeconomy’s complex operations and dynamic nature make it even more difficult. Machine Learning and Deep Learning methodologies have been investigated as alternatives to traditional forecasting methods because of recent developments in computing power and the emergence of data. How the Machine Learning and Deep Learning paradigms apply to a variety of Macro datasets have been examined in this research paper. Few Machine Learning and Deep Learning algorithms have been trained and their forecasting accuracy has been compared with that of traditional statistical method ARIMA.

Macroeconomic indicator forecasting is a difficult task and the macroeconomy’s complex operations and dynamic nature make it even more difficult. Machine Learning and Deep Learning methodologies have been investigated as alternatives to traditional forecasting methods because of recent developments in computing power and the emergence of data. How the Machine Learning and Deep Learning paradigms apply to a variety of Macro datasets have been examined in this research paper. Few Machine Learning and Deep Learning algorithms have been trained and their forecasting accuracy has been compared with that of traditional statistical method ARIMA.

Macroeconomic indicator forecasting is a difficult task and the macroeconomy’s complex operations and dynamic nature make it even more difficult. Machine Learning and Deep Learning methodologies have been investigated as alternatives to traditional forecasting methods because of recent developments in computing power and the emergence of data. How the Machine Learning and Deep Learning paradigms apply to a variety of Macro datasets have been examined in this research paper. Few Machine Learning and Deep Learning algorithms have been trained and their forecasting accuracy has been compared with that of traditional statistical method ARIMA.

Macroeconomic indicator forecasting is a difficult task and the macroeconomy’s complex operations and dynamic nature make it even more difficult. Machine Learning and Deep Learning methodologies have been investigated as alternatives to traditional forecasting methods because of recent developments in computing power and the emergence of data. How the Machine Learning and Deep Learning paradigms apply to a variety of Macro datasets have been examined in this research paper. Few Machine Learning and Deep Learning algorithms have been trained and their forecasting accuracy has been compared with that of traditional statistical method ARIMA.

Macroeconomic indicator forecasting is a difficult task and the macroeconomy’s complex operations and dynamic nature make it even more difficult. Machine Learning and Deep Learning methodologies have been investigated as alternatives to traditional forecasting methods because of recent developments in computing power and the emergence of data. How the Machine Learning and Deep Learning paradigms apply to a variety of Macro datasets have been examined in this research paper. Few Machine Learning and Deep Learning algorithms have been trained and their forecasting accuracy has been compared with that of traditional statistical method ARIMA.

Macroeconomic indicator forecasting is a difficult task and the macroeconomy’s complex operations and dynamic nature make it even more difficult. Machine Learning and Deep Learning methodologies have been investigated as alternatives to traditional forecasting methods because of recent developments in computing power and the emergence of data. How the Machine Learning and Deep Learning paradigms apply to a variety of Macro datasets have been examined in this research paper. Few Machine Learning and Deep Learning algorithms have been trained and their forecasting accuracy has been compared with that of traditional statistical method ARIMA.

Macroeconomic indicator forecasting is a difficult task and the macroeconomy’s complex operations and dynamic nature make it even more difficult. Machine Learning and Deep Learning methodologies have been investigated as alternatives to traditional forecasting methods because of recent developments in computing power and the emergence of data. How the Machine Learning and Deep Learning paradigms apply to a variety of Macro datasets have been examined in this research paper. Few Machine Learning and Deep Learning algorithms have been trained and their forecasting accuracy has been compared with that of traditional statistical method ARIMA.

Macroeconomic indicator forecasting is a difficult task and the macroeconomy’s complex operations and dynamic nature make it even more difficult. Machine Learning and Deep Learning methodologies have been investigated as alternatives to traditional forecasting methods because of recent developments in computing power and the emergence of data. How the Machine Learning and Deep Learning paradigms apply to a variety of Macro datasets have been examined in this research paper. Few Machine Learning and Deep Learning algorithms have been trained and their forecasting accuracy has been compared with that of traditional statistical method ARIMA.

Book Source: Digital Library of India Item 2015.233346 dc.contributor.author: Love Harry H. dc.date.accessioned: 2015-07-10T18:51:37Z dc.date.available: 2015-07-10T18:51:37Z dc.date.digitalpublicationdate: 2005-12-27 dc.date.citation: 1937 dc.identifier.barcode: 99999990130729 dc.identifier.origpath: /rawdataupload1/upload/0129/391 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/233346 dc.description.scanningcentre: C-DAC, Noida dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 512 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: The Commercial Press Limited, Shanghai dc.rights: Not Available dc.source.library: Ratan Tata Library, University Delhi dc.subject.classification: Natural Sciences dc.subject.keywords: Frequency dc.subject.keywords: Correlation dc.title: Application Of Statistical Methods To Agricultural Research

Biological terrorism is a threat to the security and well-being of the United States. It is critical to detect the presence of these attacks in a timely manner, in order to provide sufficient and effective responses to minimize or contain the damage inflicted. Syndromic surveillance is the process of monitoring public health-related data and applying statistical tests to determine the potential presence of a disease outbreak in the observed system. Our research involved a comparative analysis of two multivariate statistical methods, the multivariate CUSUM (MCUSUM) and the multivariate exponentially weighted moving average (MEWMA), both modified to look only for increases in disease incidence. While neither of these methods is currently in use in a biosurveillance system, they are among the most promising multivariate methods for this application. Our analysis was based on a series of simulations using synthetic syndromic surveillance data that mimics various types of background disease incidence and outbreaks. We found that, similar to results for the univariate CUSUM and EWMA, the directionally-sensitive MCUSUM and MEWMA perform very similarly.

"The Application of Statistical Methods to the Data on the Trenton Argillite Culture" is an article from American Anthropologist, Volume 18 . View more articles from American Anthropologist . View this article on JSTOR . View this article's JSTOR metadata . You may also retrieve all of this items metadata in JSON at the following URL: https://archive.org/metadata/jstor-660600

"Should the Undergraduate be Trained in Elementary Statistical Methods?" is an article from Quarterly Publications of the American Statistical Association, Volume 17 . View more articles from Quarterly Publications of the American Statistical Association . View this article on JSTOR . View this article's JSTOR metadata . You may also retrieve all of this items metadata in JSON at the following URL: https://archive.org/metadata/jstor-2965273

Tennis elbow, also known as lateral epicondylitis, is a condition characterized by pain and inflammation of the tendons that attach to the lateral epicondyle of the humerus. Lateral elbow pain affects 1 to 3% of the population, with peak incidence occurring at 40 to 50 (Bisset et al., 2011). The incidence is higher in athletes, especially overhead-throwing athletes (baseball, track and field, handball, volleyball) and tennis players, who combine forceful and repetitive extension movements (Hassebrock et al., 2019). The prevalence of lateral epicondylitis is seven times higher than medial epicondylitis (Cutts et al., 2020). The right hand has an incidence of 63%, the left one 25%, and both hands are affected in 12% of the population (Sanders et al., 2015). The recurrence rate in the population is 8.5%, and the injury may recur in an average of 19.7 months (Sanders et al., 2015). Bisset, L., Coombes, B., & Vicenzino, B. (2011). Tennis elbow. BMJ Clinical Evidence, 2011. /pmc/articles/PMC3217754/ Hassebrock, J. D., Patel, K. A., Makovicka, J. L., Chung, A. S., Tummala, S. V., Hydrick, T. C., Ginn, J. E., Hartigan, D. E., & Chhabra, A. (2019). Elbow injuries in national collegiate athletic association athletes: A 5-season epidemiological study. Orthopaedic Journal of Sports Medicine, 7(8). https://doi.org/10.1177/2325967119861959/ASSET/IMAGES/LARGE/10.1177_2325967119861959-FIG2.JPEG Cutts, S., Gangoo, S., Modi, N., & Pasapula, C. (2020). Tennis elbow: A clinical review article. Journal of Orthopaedics, 17, 203–207. https://doi.org/10.1016/J.JOR.2019.08.005 Sanders, T. L., Maradit Kremers, H., Bryan, A. J., Ransom, J. E., Smith, J., & Morrey, B. F. (2015). The epidemiology and health care burden of tennis elbow: a population-based study. The American Journal of Sports Medicine, 43(5), 1066–1071. https://doi.org/10.1177/0363546514568087

Shrinkage estimators of covariance are an important tool in modern applied and theoretical statistics. They play a key role in regularized estimation problems, such as ridge regression (aka Tykhonov regularization), regularized discriminant analysis and a variety of optimization problems. In this paper, we bring to bear the tools of random matrix theory to understand their behavior, and in particular, that of quadratic forms involving inverses of those estimators, which are important in practice. We use very mild assumptions compared to the usual assumptions made in random matrix theory, requiring only mild conditions on the moments of linear and quadratic forms in our random vectors. In particular, we show that our results apply for instance to log-normal data, which are of interest in financial applications. Our study highlights the relative sensitivity of random matrix results (and their practical consequences) to geometric assumptions which are often implicitly made by random matrix theorists and may not be relevant in data analytic practice.

We provide an introductory review of Bayesian data analytical methods, with a focus on applications for linguistics, psychology, psycholinguistics, and cognitive science. The empirically oriented researcher will benefit from making Bayesian methods part of their statistical toolkit due to the many advantages of this framework, among them easier interpretation of results relative to research hypotheses, and flexible model specification. We present an informal introduction to the foundational ideas behind Bayesian data analysis, using, as an example, a linear mixed models analysis of data from a typical psycholinguistics experiment. We discuss hypothesis testing using the Bayes factor, and model selection using cross-validation. We close with some examples illustrating the flexibility of model specification in the Bayesian framework. Suggestions for further reading are also provided.

We discuss the possibility of applying some standard statistical methods (the least square method, the maximum likelihood method, the method of statistical moments for estimation of parameters) to deterministically chaotic low-dimensional dynamic system (the logistic map) containing an observational noise. A ``pure'' Maximum Likelihood (ML) method is suggested to estimate the structural parameter of the logistic map along with the initial value $x_1$ considered as an additional unknown parameter. Comparisons with previously proposed techniques on simulated numerical examples give favorable results (at least, for the investigated combinations of sample size $N$ and noise level). Besides, unlike some suggested techniques, our method does not require the a priori knowledge of the noise variance. We also clarify the nature of the inherent difficulties in the statistical analysis of deterministically chaotic time series and the status of previously proposed Bayesian approaches. We note the trade-off between the need of using a large number of data points in the ML analysis to decrease the bias (to guarantee consistency of the estimation) and the unstable nature of dynamical trajectories with exponentially fast loss of memory of the initial condition. The method of statistical moments for the estimation of the parameter of the logistic map is discussed. This method seems to be the unique method whose consistency for deterministically chaotic time series is proved so far theoretically (not only numerically).

12 p. 28 cm

At head of title : World Health Organization, International Agency for Research on Cancer

Researchers are often faced with the prospect of dealing with observations within a given data set that are unexpected in terms of their great distance from the concentration of observations. For their potential to influence the mean disproportionately, thus affecting many statistical analyses, outlying observations require special care on the part of the researcher. It is suggested that decisions about how to go about discarding or incorporating such outliers be made with careful consideration as to the implications associated with the various procedures for doing so. Several methods for dealing with outliers are illustrated. (Contains 2 tables and 10 references.) (Author/SLD)

Researchers are often faced with the prospect of dealing with observations within a given data set that are unexpected in terms of their great distance from the concentration of observations. For their potential to influence the mean disproportionately, thus affecting many statistical analyses, outlying observations require special care on the part of the researcher. It is suggested that decisions about how to go about discarding or incorporating such outliers be made with careful consideration as to the implications associated with the various procedures for doing so. Several methods for dealing with outliers are illustrated. (Contains 2 tables and 10 references.) (Author/SLD)