Downloads & Free Reading Options - Results

We present a new approach for protecting sensitive data in a relational table (columns: attributes; rows: records). If sensitive data can be inferred by unauthorized users with non-sensitive data, we have the inference problem. We consider inference as correct classification and approach it with decision tree methods. As in our previous work, sensitive data are viewed as classes of those test data and non-sensitive data are the rest attribute values. In general, however, sensitive data may not be associated with one attribute (i.e., the class), but are distributed among many attributes. We present a generalized decision tree method for distributed sensitive data. This method takes in turn each attribute as the class and analyze the corresponding classification error. Attribute values that maximize an integrated error measure are selected for modification. Our analysis shows that modified attribute values can be restored and hence, sensitive data are not securely protected. This result implies that modified values must themselves be subjected to protection. We present methods for this ramified protection problem and also discuss other statistical attacks.

We present a new approach for protecting sensitive data in a relational table (columns: attributes; rows: records). If sensitive data can be inferred by unauthorized users with non-sensitive data, we have the inference problem. We consider inference as correct classification and approach it with decision tree methods. As in our previous work, sensitive data are viewed as classes of those test data and non-sensitive data are the rest attribute values. In general, however, sensitive data may not be associated with one attribute (i.e., the class), but are distributed among many attributes. We present a generalized decision tree method for distributed sensitive data. This method takes in turn each attribute as the class and analyze the corresponding classification error. Attribute values that maximize an integrated error measure are selected for modification. Our analysis shows that modified attribute values can be restored and hence, sensitive data are not securely protected. This result implies that modified values must themselves be subjected to protection. We present methods for this ramified protection problem and also discuss other statistical attacks.

A scenario is described which involves ill-defined elements of conflict and cooperation: the acquisition of military systems by the Department of Defense (DoD) from large corporations. Current practices lead to situations in which DoD must deal with a sole source and thus forego savings which might be realized from competition between contractors. In order to deal with this situation, the Armed Services Procurement Regulations (ASPR) prescribes procedures which must be employed in the analysis and negotiation of sole source price proposals. These time-consuming procedures generate enormous proposal backlogs for government price analysts who, because of time pressure, may not be able to do a sufficiently thorough and accurate analysis upon which to base their negotiation position. This analysis paralysis also causes payment delays which, in turn, force contractors to borrow working capital and suffer capital costs. It is clearly in the best interests of all parties to expedite the processing of these proposals. This has been accomplished by developing statistical sampling and estimation techniques which, unlike some classical procedures, are not vulnerable to exploitation through the use of clever padding strategies.

The overall aim of our research proposal is the statistical nonparametric inference of the redistribution-to-the-center estimator (RTCH) and the generalized maximum likelihood estimator (GMLE) for the survival function of a time-to- event variable that is subject to interval censoring. The RTCH, which is proposed by us, has a closed-form expression and is equal to the GMLE under a homogeneous condition. The GMLH is the standard optimal estimator in survival analysis. However, it cannot be expressed in a closed-form expression, and asymptotic distribution theory for it has been limited. From the asymptotic study of the RTCE, we have gained important insight into proofs of asymptotic properties of the GMTE. In the past four years we have established consistency, asymptotic normality and asymptotic efficiency of the GMLE under a variety of conditions. In addition, we have derived an asymptotic nonparametric two-sample distance test procedure for comparing two populations. Finally, we have begun investigating the asymptotic inference of Cox regression model for interval-censored data by establishing consistency and asymptotic normality of the %GMLE of the regression parameters under some finite assumptions. These preliminary results are being applied to a breast cancer prognostic relapse follow-up study.

Classical statistical techniques, such as multiple regression with variable selection and principal component analysis, were employed to define combinations of parameters from meteorological observations which optimally discriminate between the occurrence and nonoccurrence of thunderstorms. Routine observations of weather elements at five levels in the troposphere during two spring and summer seasons were analyzed objectively onto a 65-km grid which spanned much of the central United States. A thunderstorm occurrence was defined from manually digitized radar (MDR) observations with an MDR code of four or greater as the basis. The binary variable one or zero for occurrence or non-occurrence, respectively, was the predictand. Parameters which are measures of atmospheric moisture content, stability, and trigger mechanisms were calculated from gridded fields of surface and upper-air observed elements for different times each morning. These parameters were candidate predictors in the variable-selection procedures. Data from all grid points and for each day were pooled in order to provide an adequate sample of thunderstorm observations.

When developing simulation methodology, it is important to be able to assess the statistical efficiency of proposed estimation procedures. This paper is concerned with the assessment of the statistical efficiency of regenerative methods for simulation of networks of queues with general service times. Our estimation procedures are applicable to networks that have a single state for passage times; regenerative cycles are defined in terms of the single state. We provide a comparison of two methods for estimation of passage time characteristics in closed networks with priorities among job classes. Passage times(informally the times for a job to traverse a portion of a network) are important in computer and communication system models where they represent job response times.

Structures in many environments experience both random and harmonic excitation. A variety of closed-form techniques has been used in the aerospace industry to combine the loads resulting from the two sources. The resulting combined loads are then used to design for both yield/ultimate strength and high- cycle fatigue capability. This Technical Publication examines the cumulative distribution percentiles obtained using each method by integrating the joint probability density function of the sine and random components. A new Microsoft Excel spreadsheet macro that links with the software program Mathematica to calculate the combined value corresponding to any desired percentile is then presented along with a curve tit to this value. Another Excel macro that calculates the combination using Monte Carlo simulation is shown. Unlike the traditional techniques. these methods quantify the calculated load value with a consistent percentile. Using either of the presented methods can be extremely valuable in probabilistic design, which requires a statistical characterization of the loading. Additionally, since the CDF at high probability levels is very flat, the design value is extremely sensitive to the predetermined percentile; therefore, applying the new techniques can substantially lower the design loading without losing any of the identified structural reliability.

Language policy is crucial to any society's cultural and political fabric, as it shapes how individuals and groups communicate, interact, and identify. Language policy has important implications for nation-building and the creation of national identity. Language policy can also significantly impact university students' sense of belonging. Overt and inclusive policies can provide opportunities for students to develop language skills and cultural competencies, enhancing their academic and professional opportunities. Covert and implicit policies can create barriers to interaction between different language groups, leading to social fragmentation, exclusion, and discrimination. Overt language policies are those that are openly acknowledged and implemented, while covert policies are those that are more subtle and may be hidden. Overt language policies that promote a single national language can unite diverse groups within a nation. However, these policies can also lead to the suppression of linguistic and cultural diversity within a nation, potentially undermining the cultural richness of that society. Covert language policies can also have significant impacts on identity and national unity. For example, language policies not openly acknowledged can lead to confusion or mistrust among different language groups. These policies can reinforce power imbalances between language groups, perpetuating existing inequalities. Considering these concepts, this study aimed to answer the following questions: (1) What LP has been implemented in Kyrgyzstan? (2) How has LP affected university students? Information on the language policy implemented in Kyrgyzstan was necessary to answer the first question, which involved examining laws, banknotes, newspaper titles, and dissertation catalogs. It was necessary to gather data on how the language policy has affected the students to answer the second question. In addition, in-depth interviews with LP experts helped clarify both questions. The collected information was analyzed using textual analysis and statistical methods. Data analysis showed that different models of language policy were implemented in Kyrgyzstan, including multilingualism, bilingualism, and monolingualism. Overt, covert, and vague LP were used to promote these models. These models and methods have formed segregated communities, language discrimination, and different linguistic identities within the same ethnic groups.

Language policy is crucial to any society's cultural and political fabric, as it shapes how individuals and groups communicate, interact, and identify. Language policy has important implications for nation-building and the creation of national identity. Language policy can also significantly impact university students' sense of belonging. Overt and inclusive policies can provide opportunities for students to develop language skills and cultural competencies, enhancing their academic and professional opportunities. Covert and implicit policies can create barriers to interaction between different language groups, leading to social fragmentation, exclusion, and discrimination. Overt language policies are those that are openly acknowledged and implemented, while covert policies are those that are more subtle and may be hidden. Overt language policies that promote a single national language can unite diverse groups within a nation. However, these policies can also lead to the suppression of linguistic and cultural diversity within a nation, potentially undermining the cultural richness of that society. Covert language policies can also have significant impacts on identity and national unity. For example, language policies not openly acknowledged can lead to confusion or mistrust among different language groups. These policies can reinforce power imbalances between language groups, perpetuating existing inequalities. Considering these concepts, this study aimed to answer the following questions: (1) What LP has been implemented in Kyrgyzstan? (2) How has LP affected university students? Information on the language policy implemented in Kyrgyzstan was necessary to answer the first question, which involved examining laws, banknotes, newspaper titles, and dissertation catalogs. It was necessary to gather data on how the language policy has affected the students to answer the second question. In addition, in-depth interviews with LP experts helped clarify both questions. The collected information was analyzed using textual analysis and statistical methods. Data analysis showed that different models of language policy were implemented in Kyrgyzstan, including multilingualism, bilingualism, and monolingualism. Overt, covert, and vague LP were used to promote these models. These models and methods have formed segregated communities, language discrimination, and different linguistic identities within the same ethnic groups.

Cluster analysis provides a tool for mapping out regions of ambiguous response in sparse array beam forming problems. This paper discusses clustering and its application to matched field processing (MFP) problems in ocean acoustics. The map of the ambiguity volume provided by clustering can be used for improved interpretation and postprocessing. By peak-picking in cluster space, rather than in spatial dimensions, the authors are able to identify and discard ambiguous peaks that result from the presence of a strong source. As shown in a data example, clustering can help collapse three-dimensional MFP output to bearings-only while preserving signal gains obtained by accounting for multipath. Clustering also can provide computational gains by allowing the elimination of highly redundant replicas.

No Abstract Available

No Abstract Available

"The Value to Economics of Formal Statistical Methods" is an article from Publications of the American Statistical Association, Volume 14 . View more articles from 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-2964896

The paper provides a survey of results in statistical inference in systems reliability using Bernoulli sampling of individual components. Particular attention is given to the notion of Buehler optimally and its implementation in such problems. Recent results of the authors on Buehler optimal confidence bounds on the reliability of series and parallel systems are discussed. For series systems, these results employ a generalization of an inequality of Sudakov. For parallel systems. Buehler optimal bounds are obtained for small numbers of failures using the notion of Schur concavity. Estimates of the optimal bounds are obtained in those cases for which the property of Schur concavity does not hold. (Author)

The paper provides a survey of results in statistical inference in systems reliability using Bernoulli sampling of individual components. Particular attention is given to the notion of Buehler optimally and its implementation in such problems. Recent results of the authors on Buehler optimal confidence bounds on the reliability of series and parallel systems are discussed. For series systems, these results employ a generalization of an inequality of Sudakov. For parallel systems. Buehler optimal bounds are obtained for small numbers of failures using the notion of Schur concavity. Estimates of the optimal bounds are obtained in those cases for which the property of Schur concavity does not hold. (Author)

While breast cancer in many families is due to the known breast cancer genes, there is still a sizable proportion of families that do not show linkage to these known genes. Thus, it is important that other genetic loci responsible for these cases be investigated. The past year was mostly spent on developing theory for estimating linkage disequilibrium between a generalized mendelian locus and a nearby marker locus (Task 4). Work is progressing satisfactorily and results will be implemented in a computer program. A manuscript will be written in the next funding period.

While breast cancer in many families is due to the known breast cancer genes, there is still a sizable proportion of families that do not show linkage to these known genes. Thus, it is important that other genetic loci responsible for these cases be investigated. The past year was mostly spent on developing theory for estimating linkage disequilibrium between a generalized mendelian locus and a nearby marker locus (Task 4). Work is progressing satisfactorily and results will be implemented in a computer program. A manuscript will be written in the next funding period.

"Davenport's Statistical Methods" is an article from The American Naturalist, Volume 33 . View more articles from The American Naturalist . 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-2453984

"Davenport's Statistical Methods" is an article from The American Naturalist, Volume 33 . View more articles from The American Naturalist . 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-2453984

To understand their data better, astronomers need to use statistical tools that are more advanced than traditional ``freshman lab'' statistics. As an illustration, the problem of combining apparently incompatible measurements of a quantity is presented from both the traditional, and a more sophisticated Bayesian, perspective. Explicit formulas are given for both treatments. Results are shown for the value of the Hubble Constant, and a 95% confidence interval of 66 < H0 < 82 (km/s/Mpc) is obtained.

A neutrosophic statistic is a random variable and it has a neutrosophic probability distribution. So, in this paper, we introduce the new neutrosophic Birnbaum–Saunders distribution. Some statistical properties are derived, using Mathematica 13.1.1 and R-Studio Software. Two different estimation methods for parameters estimation are introduced for new distribution: maximum likelihood estimation method and Bayesian estimation method. A Monte-Carlo simulation study is used to investigate the behavior of parameters estimates of new distribution, compare the performance of different estimates, and compare between our distribution and the classical version of Birnbaum-Saunders. Finally, study the validity of our new distribution in real life.

x, 829 pages ; 21 cm

x, 829 pages ; 21 cm

x, 829 pages ; 21 cm

x, 829 pages ; 21 cm

This paper documents an approach to conceptual and preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically design of experiments (DOE) and response surface methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an overall evaluation criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in a innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting a solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a high speed civil transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabalistic designs (and eventually robust ones).

This paper documents an approach to conceptual and preliminary aircraft design in which system synthesis is achieved using statistical methods, specifically design of experiments (DOE) and response surface methodology (RSM). These methods are employed in order to more efficiently search the design space for optimum configurations. In particular, a methodology incorporating three uses of these techniques is presented. First, response surface equations are formed which represent aerodynamic analyses, in the form of regression polynomials, which are more sophisticated than generally available in early design stages. Next, a regression equation for an overall evaluation criterion is constructed for the purpose of constrained optimization at the system level. This optimization, though achieved in a innovative way, is still traditional in that it is a point design solution. The methodology put forward here remedies this by introducing uncertainty into the problem, resulting a solutions which are probabilistic in nature. DOE/RSM is used for the third time in this setting. The process is demonstrated through a detailed aero-propulsion optimization of a high speed civil transport. Fundamental goals of the methodology, then, are to introduce higher fidelity disciplinary analyses to the conceptual aircraft synthesis and provide a roadmap for transitioning from point solutions to probabalistic designs (and eventually robust ones).

Book Source: Digital Library of India Item 2015.13960 dc.contributor.author: Frank A.pearson dc.date.accessioned: 2015-06-23T22:36:24Z dc.date.available: 2015-06-23T22:36:24Z dc.date.digitalpublicationdate: 2007-09-26 dc.date.citation: 1942 dc.identifier.barcode: 5990010155119 dc.identifier.origpath: /data1/upload/0009/876 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/13960 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 449 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher: John Wiley Amp Sons New York dc.rights: In Public Domain dc.source.library: University Of Allahabad dc.subject.classification: Literature dc.title: Statistical Methods Applied To Agricultural Economics

Book Source: Digital Library of India Item 2015.6075 dc.contributor.author: Johnson Poalmer .o dc.date.accessioned: 2015-06-19T15:31:05Z dc.date.available: 2015-06-19T15:31:05Z dc.date.digitalpublicationdate: 2005-11-16 dc.date.citation: 1949 dc.identifier.barcode: 99999990174598 dc.identifier.origpath: /data1/upload/0028/908 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/6075 dc.description.scanningcentre: C-DAC, Noida dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 402 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: Prentice Hall Inc New York dc.rights: 1949, By Prentice-hall, Inc. 70 Fifth Avenue, New York dc.source.library: Ratan Tata Library, University Delhi dc.subject.classification: Natural Sciences dc.subject.keywords: Probability dc.subject.keywords: Statistical dc.title: Statistical Methods In Research

Book Source: Digital Library of India Item 2015.261364 dc.contributor.author: Frederick Cecil Mills dc.date.accessioned: 2015-07-22T18:26:32Z dc.date.available: 2015-07-22T18:26:32Z dc.date.citation: 1924 dc.identifier.barcode: 05990010884131 dc.identifier.origpath: /data58/upload/0085/848 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/261364 dc.description.scannerno: 20003265 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 768 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: Henry Holt And Co New York dc.source.library: ECC LIBRARY ALLAHABAD dc.subject.classification: Literature dc.title: Statistical Methods dc.type: ptiff dc.type: pdf

Book Source: Digital Library of India Item 2015.261364 dc.contributor.author: Frederick Cecil Mills dc.date.accessioned: 2015-07-22T18:26:32Z dc.date.available: 2015-07-22T18:26:32Z dc.date.citation: 1924 dc.identifier.barcode: 05990010884131 dc.identifier.origpath: /data58/upload/0085/848 dc.identifier.copyno: 1 dc.identifier.uri: http://www.new.dli.ernet.in/handle/2015/261364 dc.description.scannerno: 20003265 dc.description.scanningcentre: IIIT, Allahabad dc.description.main: 1 dc.description.tagged: 0 dc.description.totalpages: 768 dc.format.mimetype: application/pdf dc.language.iso: English dc.publisher.digitalrepublisher: Digital Library Of India dc.publisher: Henry Holt And Co New York dc.source.library: ECC LIBRARY ALLAHABAD dc.subject.classification: Literature dc.title: Statistical Methods dc.type: ptiff dc.type: pdf

This article is from Malaria Journal , volume 13 . Abstract Background: Within affected communities, Plasmodium falciparum infections may be skewed in distribution such that single or small clusters of households consistently harbour a disproportionate number of infected individuals throughout the year. Identifying these hotspots of malaria transmission would permit targeting of interventions and a more rapid reduction in malaria burden across the whole community. This study set out to compare different statistical methods of hotspot detection (SaTScan, kernel smoothing, weighted local prevalence) using different indicators (PCR positivity, AMA-1 and MSP-1 antibodies) for prediction of infection the following year. Methods: Two full surveys of four villages in Mwanza, Tanzania were completed over consecutive years, 2010-2011. In both surveys, infection was assessed using nested polymerase chain reaction (nPCR). In addition in 2010, serologic markers (AMA-1 and MSP-119 antibodies) of exposure were assessed. Baseline clustering of infection and serological markers were assessed using three geospatial methods: spatial scan statistics, kernel analysis and weighted local prevalence analysis. Methods were compared in their ability to predict infection in the second year of the study using random effects logistic regression models, and comparisons of the area under the receiver operating curve (AUC) for each model. Sensitivity analysis was conducted to explore the effect of varying radius size for the kernel and weighted local prevalence methods and maximum population size for the spatial scan statistic. Results: Guided by AUC values, the kernel method and spatial scan statistics appeared to be more predictive of infection in the following year. Hotspots of PCR-detected infection and seropositivity to AMA-1 were predictive of subsequent infection. For the kernel method, a 1 km window was optimal. Similarly, allowing hotspots to contain up to 50% of the population was a better predictor of infection in the second year using spatial scan statistics than smaller maximum population sizes. Conclusions: Clusters of AMA-1 seroprevalence or parasite prevalence that are predictive of infection a year later can be identified using geospatial models. Kernel smoothing using a 1 km window and spatial scan statistics both provided accurate prediction of future infection.

This standard has been duly incorporated into federal law per the following sections of the Code of Federal Regulations: 40 CFR § 1090.95 . This document was posted by Public.Resource.Org, which is not affiliated with nor authorized by the United States government, the State governments, or the American Society for Testing and Materials (ASTM). We posted this document in order to allow citizens to read the laws that govern us. By order of the Honorable U.S. Court of Appeals for the DC Circuit in their unanimous opinion of September 12, 2023 in Public.Resource.Org v. ASTM et. al.

For analyzing the intra-annual distribution of incidence in comparison with a number of years and for various territories, it is expedient to calculate the monthly incidence in indices per 10,000 or 100,000 of population from specific age contingents. A reliable determination of the onset of the period of the seasonal rise in incidence can be obtained by a statistical analysis of the intra-annual distribution of incidence on the basis of evaluating the significance in the difference of indices. For a quantitative evaluation of the development of incidence during the period of the seasonal rise it is possible to use the indices for the average tempo of dynamics of incidence, calculated by the formula of the weighted mean geometric coefficient of increase in incidence, taking into consideration the indices of incidence in each of the months of this period. This same method may be used for the quantitative characteristics of the dynamics of incidence during the period of the seasonal decline. As a criterion for the quantitative characteristics of incidence levels during the period of the seasonal rise, and also decline, in incidence, it is possible to use the intensive indices of incidence during these periods.

For analyzing the intra-annual distribution of incidence in comparison with a number of years and for various territories, it is expedient to calculate the monthly incidence in indices per 10,000 or 100,000 of population from specific age contingents. A reliable determination of the onset of the period of the seasonal rise in incidence can be obtained by a statistical analysis of the intra-annual distribution of incidence on the basis of evaluating the significance in the difference of indices. For a quantitative evaluation of the development of incidence during the period of the seasonal rise it is possible to use the indices for the average tempo of dynamics of incidence, calculated by the formula of the weighted mean geometric coefficient of increase in incidence, taking into consideration the indices of incidence in each of the months of this period. This same method may be used for the quantitative characteristics of the dynamics of incidence during the period of the seasonal decline. As a criterion for the quantitative characteristics of incidence levels during the period of the seasonal rise, and also decline, in incidence, it is possible to use the intensive indices of incidence during these periods.

For analyzing the intra-annual distribution of incidence in comparison with a number of years and for various territories, it is expedient to calculate the monthly incidence in indices per 10,000 or 100,000 of population from specific age contingents. A reliable determination of the onset of the period of the seasonal rise in incidence can be obtained by a statistical analysis of the intra-annual distribution of incidence on the basis of evaluating the significance in the difference of indices. For a quantitative evaluation of the development of incidence during the period of the seasonal rise it is possible to use the indices for the average tempo of dynamics of incidence, calculated by the formula of the weighted mean geometric coefficient of increase in incidence, taking into consideration the indices of incidence in each of the months of this period. This same method may be used for the quantitative characteristics of the dynamics of incidence during the period of the seasonal decline. As a criterion for the quantitative characteristics of incidence levels during the period of the seasonal rise, and also decline, in incidence, it is possible to use the intensive indices of incidence during these periods.

For analyzing the intra-annual distribution of incidence in comparison with a number of years and for various territories, it is expedient to calculate the monthly incidence in indices per 10,000 or 100,000 of population from specific age contingents. A reliable determination of the onset of the period of the seasonal rise in incidence can be obtained by a statistical analysis of the intra-annual distribution of incidence on the basis of evaluating the significance in the difference of indices. For a quantitative evaluation of the development of incidence during the period of the seasonal rise it is possible to use the indices for the average tempo of dynamics of incidence, calculated by the formula of the weighted mean geometric coefficient of increase in incidence, taking into consideration the indices of incidence in each of the months of this period. This same method may be used for the quantitative characteristics of the dynamics of incidence during the period of the seasonal decline. As a criterion for the quantitative characteristics of incidence levels during the period of the seasonal rise, and also decline, in incidence, it is possible to use the intensive indices of incidence during these periods.

For analyzing the intra-annual distribution of incidence in comparison with a number of years and for various territories, it is expedient to calculate the monthly incidence in indices per 10,000 or 100,000 of population from specific age contingents. A reliable determination of the onset of the period of the seasonal rise in incidence can be obtained by a statistical analysis of the intra-annual distribution of incidence on the basis of evaluating the significance in the difference of indices. For a quantitative evaluation of the development of incidence during the period of the seasonal rise it is possible to use the indices for the average tempo of dynamics of incidence, calculated by the formula of the weighted mean geometric coefficient of increase in incidence, taking into consideration the indices of incidence in each of the months of this period. This same method may be used for the quantitative characteristics of the dynamics of incidence during the period of the seasonal decline. As a criterion for the quantitative characteristics of incidence levels during the period of the seasonal rise, and also decline, in incidence, it is possible to use the intensive indices of incidence during these periods.

For analyzing the intra-annual distribution of incidence in comparison with a number of years and for various territories, it is expedient to calculate the monthly incidence in indices per 10,000 or 100,000 of population from specific age contingents. A reliable determination of the onset of the period of the seasonal rise in incidence can be obtained by a statistical analysis of the intra-annual distribution of incidence on the basis of evaluating the significance in the difference of indices. For a quantitative evaluation of the development of incidence during the period of the seasonal rise it is possible to use the indices for the average tempo of dynamics of incidence, calculated by the formula of the weighted mean geometric coefficient of increase in incidence, taking into consideration the indices of incidence in each of the months of this period. This same method may be used for the quantitative characteristics of the dynamics of incidence during the period of the seasonal decline. As a criterion for the quantitative characteristics of incidence levels during the period of the seasonal rise, and also decline, in incidence, it is possible to use the intensive indices of incidence during these periods.

The reliability of the mean-field approach to polymer statistical mechanics is investigated by comparing results from a recently developed lattice mean-field theory (LMFT) method to statistically exact results from two independent numerical Monte Carlo simulations for the problems of a polymer chain moving in a spherical cavity and a polymer chain partitioning between two confining spheres of different radii. It is shown that in some cases the agreement between the LMFT and the simulation results is excellent, while in others, such as the case of strongly fluctuating monomer repulsion fields, the LMFT results agree with the simulations only qualitatively. Various approximations of the LMFT method are systematically estimated, and the quantitative discrepancy between the two sets of results is explained with the diminished accuracy of the saddle-point approximation, implicit in the mean-field method, in the case of strongly fluctuating fields.

The most widely used task fMRI analyses use parametric methods that depend on a variety of assumptions. While individual aspects of these fMRI models have been evaluated, they have not been evaluated in a comprehensive manner with empirical data. In this work, a total of 2 million random task fMRI group analyses have been performed using resting state fMRI data, to compute empirical familywise error rates for the software packages SPM, FSL and AFNI, as well as a standard non-parametric permutation method. While there is some variation, for a nominal familywise error rate of 5% the parametric statistical methods are shown to be conservative for voxel-wise inference and invalid for cluster-wise inference; in particular, cluster size inference with a cluster defining threshold of p = 0.01 generates familywise error rates up to 60%. We conduct a number of follow up analyses and investigations that suggest the cause of the invalid cluster inferences is spatial auto correlation functions that do not follow the assumed Gaussian shape. By comparison, the non-parametric permutation test, which is based on a small number of assumptions, is found to produce valid results for voxel as well as cluster wise inference. Using real task data, we compare the results between one parametric method and the permutation test, and find stark differences in the conclusions drawn between the two using cluster inference. These findings speak to the need of validating the statistical methods being used in the neuroimaging field.

We develop a Bayesian model for globular clusters composed of multiple stellar populations, extending earlier statistical models for open clusters composed of simple (single) stellar populations (vanDyk et al. 2009, Stein et al. 2013). Specifically, we model globular clusters with two populations that differ in helium abundance. Our model assumes a hierarchical structuring of the parameters in which physical properties---age, metallicity, helium abundance, distance, absorption, and initial mass---are common to (i) the cluster as a whole or to (ii) individual populations within a cluster, or are unique to (iii) individual stars. An adaptive Markov chain Monte Carlo (MCMC) algorithm is devised for model fitting that greatly improves convergence relative to its precursor non-adaptive MCMC algorithm. Our model and computational tools are incorporated into an open-source software suite known as BASE-9. We use numerical studies to demonstrate that our method can recover parameters of two-population clusters, and also show model misspecification can potentially be identified. As a proof of concept, we analyze the two stellar populations of globular cluster NGC 5272 using our model and methods. (BASE-9 is available from GitHub: https://github.com/argiopetech/base/releases).

The Taiwanese-American Occultation Survey (TAOS) will detect objects in the Kuiper Belt, by measuring the rate of occultations of stars by these objects, using an array of three to four 50cm wide-field robotic telescopes. Thousands of stars will be monitored, resulting in hundreds of millions of photometric measurements per night. To optimize the success of TAOS, we have investigated various methods of gathering and processing the data and developed statistical methods for detecting occultations. In this paper we discuss these methods. The resulting estimated detection efficiencies will be used to guide the choice of various operational parameters determining the mode of actual observation when the telescopes come on line and begin routine observations. In particular we show how real-time detection algorithms may be constructed, taking advantage of having multiple telescopes. We also discuss a retrospective method for estimating the rate at which occultations occur.

Developed by B. Efron (1979) and his colleagues (P. Diaconis and B. Efron, 1983), bootstrap methods have the goal of creating an empirical sampling distribution that can be used to test statistical hypotheses, estimate standard errors, and create confidence intervals. Bootstrapping methods offer a unique and effective method for testing the stability and replicability of results. This paper explains the bootstrap method of exploring replicability internally, including a heuristic example applying bootstrap methods to a confirmatory factor analysis, using the Statistical Package for the Social Sciences and AMOS. (Contains 1 figure, 5 tables, and 13 references.) (Author/SLD)

Climate change is a reality of today. Paleoclimatic proxies and climate predictions based on coupled atmosphere-ocean general circulation models provide us with temperature data. Using Detrended Fluctuation Analysis, we are investigating the statistical connection between the climate types of the present and these local temperatures. We are relating this issue to some well-known historic climate shifts. Our main result is that the temperature fluctuations with or without a temperature scale attached to them, can be used to classify climates in the absence of other indicators such as pan evaporation and precipitation.

In this paper, we investigate the problem of assessing statistical methods and effectively summarizing results from simulations. Specifically, we consider problems of the type where multiple methods are compared on a reasonably large test set of problems. These simulation studies are typically used to provide advice on an effective method for analyzing future untested problems. Most of these simulation studies never apply statistical methods to find which method(s) are expected to perform best. Instead, conclusions are based on a qualitative assessment of poorly chosen graphical and numerical summaries of the results. We illustrate that the Empirical Cumulative Distribution Function when used appropriately is an extremely effective tool for assessing what matters in large scale statistical simulations.

In this paper, we investigate the problem of assessing statistical methods and effectively summarizing results from simulations. Specifically, we consider problems of the type where multiple methods are compared on a reasonably large test set of problems. These simulation studies are typically used to provide advice on an effective method for analyzing future untested problems. Most of these simulation studies never apply statistical methods to find which method(s) are expected to perform best. Instead, conclusions are based on a qualitative assessment of poorly chosen graphical and numerical summaries of the results. We illustrate that the Empirical Cumulative Distribution Function when used appropriately is an extremely effective tool for assessing what matters in large scale statistical simulations.

In this paper, we investigate the problem of assessing statistical methods and effectively summarizing results from simulations. Specifically, we consider problems of the type where multiple methods are compared on a reasonably large test set of problems. These simulation studies are typically used to provide advice on an effective method for analyzing future untested problems. Most of these simulation studies never apply statistical methods to find which method(s) are expected to perform best. Instead, conclusions are based on a qualitative assessment of poorly chosen graphical and numerical summaries of the results. We illustrate that the Empirical Cumulative Distribution Function when used appropriately is an extremely effective tool for assessing what matters in large scale statistical simulations.

Zoomable full resolution image available at davidrumsey.com . This image 4521G is a part of image group 4521.000 . Last page of preface. See note field above. P1331; US Atlases 408.

The computational challenge of automating outlier, or blunder point, detection in radio metric data requires the use of nonstandard statistical methods because the outliers have a deleterious effect on standard least squares methods. The particular nonstandard methods most applicable to the task are the robust statistical techniques that have undergone intense development since the 1960s. These new methods are by design more resistant to the effects of outliers than standard methods. Because the topic may be unfamiliar, a brief introduction to the philosophy and methods of robust statistics is presented. Then the application of these methods to the automated outlier detection problem is detailed for some specific examples encountered in practice.