Showing papers in "The Statistician in 1994"
TL;DR: Representation and Geometry of Multivariate Data.
3,968 citations
TL;DR: A survey of approaches to measurement in socobehavioral research can be found in this paper, where the authors present a survey of the most common approaches to measuring in sociology research.
Abstract: Contents: Preface. Overview. Part I: Measurement. Measurement and Scientific Inquiry. Criterion-Related Validation. Construct Validation. Reliability. Selected Approaches to Measurement in Sociobehavioral Research. Part II: Design. Science and Scientific Inquiry. Definitions and Variables. Theories, Problems, and Hypotheses. Research Design: Basic Principles and Concepts. Artifacts and Pitfalls in Research. Experimental Designs. Quasi-Experimental Designs. Nonexperimental Designs. Introduction to Sampling. Part III: Analysis. Computers and Computer Programs. Simple Regression Analysis. Multiple Regression Analysis. A Categorical Independent Variable. Multiple Categorical Independent Variables: Factorial Designs. Attribute--Treatments--Interactions Analysis of Covariance. Exploratory Factor Analysis. Confirmatory Factor Analysis. Structural Equation Modeling. Appendices: Critical Values for F. Percentile Points for X2 Distribution.
3,942 citations
1,581 citations
1,269 citations
711 citations
TL;DR: This work describes some general purpose software that is currently developing for implementing Gibbs sampling: BUGS (Bayesian inference using Gibbs sampling), written in Modula-2 and runs under both DOS and UNIX.
Abstract: Gibbs sampling has enormous potential for analysing complex data sets However, routine use of Gibbs sampling has been hampered by the lack of general purpose software for its implementation Until now all applications have involved writing one-off computer code in low or intermediate level languages such as C or Fortran We describe some general purpose software that we are currently developing for implementing Gibbs sampling: BUGS (Bayesian inference using Gibbs sampling) The BUGS system comprises three components: first, a natural language for specifying complex models; second, an 'expert system' for deciding appropriate methods for obtaining samples required by the Gibbs sampler; third, a sampling module containing numerical routines to perform the sampling S objects are used for data input and output BUGS is written in Modula-2 and runs under both DOS and UNIX
691 citations
534 citations
357 citations
TL;DR: In this paper, the authors present a new test theory based on Constructed-Response Testing, Artificial Intelligence, and Model-Based Measurement, which they call New Test Theory.
Abstract: Contents: R.J. Mislevy, Introduction. R.E. Snow, D.F. Lohman, Cognitive Psychology, New Test Design, and New Test Theory: An Introduction. R.J. Mislevy, Foundations of a New Test Theory. D.F. Lohman, M.J. Ippel, Cognitive Diagnosis: From Statistically Based Assessment Toward Theory-Based Assessment. N.S. Cole, Comments on Chapters 1-3. D. Thissen, Repealing Rules That No Longer Apply to Psychological Measurement. R.E. Bennett, Toward Intelligent Assessment: An Integration of Constructed-Response Testing, Artificial Intelligence, and Model-Based Measurement. S. Embretson, Psychometric Models for Learning and Cognitive Processes. B.F. Green, Comments on Chapters 4-6. S.P. Marshall, Assessing Schema Knowledge. P.J. Feltovich, R.J. Spiro, R.L. Coulson, Learning, Teaching, and Testing for Complex Conceptual Understanding. G.N. Masters, R.J. Mislevy, New Views of Student Learning: Implications for Educational Measurement. D.H. Gitomer, D. Rock, Addressing Process Variables in Test Analysis. J.B. Carroll, Comments on Chapters 7-10. K. Yamamoto, D.H. Gitomer, Application of a HYBRID Model to a Test of Cognitive Skill Representation. J.B. Carroll, Test Theory and the Behavioral Scaling of Test Performance. I.I. Bejar, A Generative Approach to Psychological and Educational Measurement. E.H. Haertel, D.E. Wiley, Representations of Ability Structures: Implications for Testing. H.I. Braun, Comments on Chapters 11-14.
TL;DR: In this article, the authors present an analysis of a two-factor experiment with two treatments and two experiments at different sites in the US and Europe, showing that the regression is linear regression.
Abstract: INTRODUCTION The Need for Statistics Types of Data The Use of Computers in Statistics PROBABILITY AND DISTRIBUTIONS Probability Populations and Samples Means and Variances The Normal Distribution Sampling Distributions ESTIMATION AND HYPOTHESIS TESTING Estimation of the Population Mean Testing Hypotheses about the Population Mean Population Variance Unknown Comparison of Samples A Pooled Estimate of Variance A SIMPLE EXPERIMENT Randomization and Replication Analysis of a Completely Randomized Design with Two Treatments A Completely Randomized Design with Several Treatments Testing Overall Variation Between the Treatments CONTROL OF RANDOM VARIATION BY BLOCKING Local Control of Variation Analysis of a Randomized Block Design Meaning of the Error Mean Square Latin Square Designs Multiple Latin Squares Design The Benefit of Blocking and the Use of Natural Blocks PARTICULAR QUESTIONS ABOUT TREATMENTS Treatment Structure Treatment Contrasts Factorial Treatment Structure Main Effects and Interactions Analysis of Variance for a Two-Factor Experiment Partial Factorial Structure Comparing Treatment Means - Are Multiple Comparison Methods Helpful? MORE ON FACTORIAL TREATMENT STRUCTURE More than Two Factors Factors with Two Levels The Double Benefit of Factorial Structure Many Factors and Small Blocks The Analysis of Confounded Experiments Split Plot Experiments Analysis of a Split Plot Experiment Experiments Repeated at Different Sites THE ASSUMPTIONS BEHIND THE ANALYSIS Our Assumptions Normality Variance Homogeneity Additivity Transformations of Data for Theoretical Reasons A More General Form of Analysis Empirical Detection of the Failure of Assumptions and Selection of Appropriate Transformations Practice and Presentation STUDYING LINEAR RELATIONSHIPS Linear Regression Assessing the Regression Line Inferences about the Slope of a Line Prediction Using a Regression Line Correlation Testing Whether the Regression is Linear Regression Analysis Using Computer Packages MORE COMPLEX RELATIONSHIPS Making the Crooked Straight Two Independent Variables Testing the Components of a Multiple Relationship Multiple Regression Possible Problems in Computer Multiple Regression LINEAR MODELS The Use of Models Models for Factors and Variables Comparison of Regressions Fitting Parallel Lines Covariance Analysis Regression in the Analysis of Treatment Variation NONLINEAR MODELS Advantages of Linear and Nonlinear Models Fitting Nonlinear Models to Data Inferences about Nonlinear Parameters Exponential Models Inverse Polynomial Models Logistic Models for Growth Curves THE ANALYSIS OF PROPORTIONS Data in the Form of Frequencies The 2 ' 2 Contingency Table More than Two Situations or More than Two Outcomes General Contingency Tables Estimation of Proportions Sample Sizes for Estimating Proportions MODELS AND DISTRIBUTIONS FOR FREQUENCY DATA Models for Frequency Data Testing the Agreement of Frequency Data with Simple Models Investigating More Complex Models The Binomial Distribution The Poisson Distribution Generalized Models for Analyzing Experimental Data Log-Linear Models Logit Analysis of Response Data MAKING AND ANALYZING SEVERAL EXPERIMENTAL MEASUREMENTS Different Measurements on the Same Units Interdependence of Different Variables Repeated Measurements Joint (Bivariate) Analysis Indices of Combined Yield Investigating Relationships with Experimental Data ANALYZING AND SUMMARIZING MANY MEASUREMENTS Introduction to Multivariate Data Principal Component Analysis Covariance or Correlation Matrix Cluster Analysis Similarity and Dissimilarity Measures Hierarchical Clustering Comparison of PCA and Cluster Analysis CHOOSING THE MOST APPROPRIATE EXPERIMENTAL DESIGN The Components of Design Units and Treatments Replication and Precision Different Levels of Variation and Within-Unit Replication Variance Components and Split Plot Designs Randomization Managing with Limited Resources Factors with Quantitative Levels Screening and Selection On-Farm Experiments SAMPLING FINITE POPULATIONS Experiments and Sample Surveys Simple Random Sampling Stratified Random Sampling Cluster Sampling, Multistage Sampling and Sampling Proportional to Size Ratio and Regression Estimates REFERENCES APPENDIX INDEX
TL;DR: In this paper, the authors consider the problem of identifying the probability distributions of real-valued random variables based on some Funcions of them, and they show that some models for Reliability and Survival Analysis can be found in some Econometric Models.
Abstract: Introduction. Identifiability of Probability Distributions of Real-Valued Random Variables Based on Some Funcions of Them. Identifiability of Probability Measures on Abstract Spaces. Identifiability for Some Types of Stochastic Processes. Generalized Convolutions. Identifiability in Some Econometric Models. Identifiability in Some Models for Reliability and Survival Analysis. Identifiability for Mixtures of Distributions. Chapter References. Index.
TL;DR: The logic and language of social research and inferential data analysis in social research, from sample to population to tests of significance for categoric variables and more.
Abstract: Preface to the second editionAcknowledgementsPart one: The logic and language of social researchIntroducing data analysisThe logic of data analysisPart two: From data collection to computerPreparing the dataGetting to know the computerDOS, windows and SPSSPart three: Descriptive data analysis in social researchFrom computer to analysisdescribing single variablesUnivariate descriptive statistics using SPSSBivariate analysis for categoric variablesmeasures of associationBivariate analysis for interval level variablesregression and correlationPart four: Inferential data analysis in social researchFrom sample to populationthe idea of inferential statisticsTests of significance for categoric variablesPart five: Introduction to multivariate analysisGeneral linear modelsmultivariate analysisLongitudinal datatheir collection and analysisConcluding remarksAppendixGlossary ReferencesIndex.
TL;DR: The multinomial-Poisson transformation as mentioned in this paper simplifies maximum likelihood estimation in a wide variety of models for multiinomial data, including log-linear models, capture-recapture models, proportional hazards models with categorical covariates and generalizations of the Rasch model.
Abstract: SUMMARY The multinomial-Poisson (MP) transformation simplifies maximum likelihood estimation in a wide variety of models for multinomial data. On the basis of specialized derivations, investigators have applied the MP transformation to various models. Here we present a general derivation, which is simpler than the specialized derivations and allows investigators to use the MP transformation readily in new models. We also show how the MP transformation can accommodate incomplete multinomial data and how it can assist in finding closed form maximum likelihood estimates and variances. Previous applications include log-linear models, capture-recapture models, proportional hazards models with categorical covariates and generalizations of the Rasch model. New applications include computing the variance of the logarithm of the odds ratio, a model for voter plurality, conditional logistic regression for matched sets and two-stage case-control studies.
TL;DR: In this paper, Boostrap procedures for computing lower and upper confidence limits for the mean of a log-normal distribution based on complete samples are presented, which yield confidence bounds that are often nearly equal to the optimal unbiased bounds that require complex numerical algorithms.
Abstract: SUMMARY Boostrap procedures for computing lower and upper confidence limits for the mean of a log-normal distribution based on complete samples are presented. The procedures are based on an approximate pivotal statistic and are shown to yield confidence bounds that are often nearly equal to the optimal (uniformly most accurate unbiased) bounds that require complex numerical algorithms.
TL;DR: In this article, a structuralist view of measurement is presented, which is an extension of the Received Measurement Theories (see, e.g., Section 5.1).
Abstract: Contents: C.W. Savage, P. Ehrlich, A Brief Introduction to Measurement and to Essays. R.D. Luce, L. Narens, Intrinsic Archimedeanness and the Continuum. P. Suppes, M. Zanotti, Qualitative Axioms for Random-Variable Representation of Extensive Quantities. E.W. Adams, On the Empirical Status of Measurement Axioms: The Case of Subjective Probability. H.E. Kyburg Jr., Measuring Errors of Measurement. W. Balzer, The Structuralist View of Measurement: An Extension of Received Measurement Theories. J.P. Burgess, Synthetic Physics and Nominalist Realism. A. Koslow, Quantitative But Nonnumerical Relations in Sciene: Eudoxus, Newton, and Maxwell. B. Ellis, Conventionalism in Measurement Theory. K. Berka, Are There Objective Grounds for Measurement Procedures? Z. Domotor, Measurement from Empiricist and Realist Points of View.
TL;DR: Introduction to Statistical Testing Examples of Test Procedures List of Tests Classification of Tests The Tests List of Tables Tables
Abstract: Introduction to Statistical Testing Examples of Test Procedures List of Tests Classification of Tests The Tests List of Tables Tables
TL;DR: In this paper, an alternative method of sample size determination that satisfies the average power criterion is outlined, which is a useful criterion for judging the performance of a sample size estimation procedure, when there is uncertainty about effect size.
Abstract: The use of an empirical estimate of effect size in the standard sample size formula introduces variability in the power to detect the true underlying effect. A useful criterion for judging the performance of a sample size estimation procedure, when there is uncertainty about effect size, is that the average power supplied to those studies in a discipline seeking a particular level of power should reach the nominal level. The common informal practice of substituting an effect size estimate from an earlier study in the traditional sample size formula fails to meet this criterion, yielding an average power that falls below the nominal level, often by a considerable margin. An alternative method of sample size determination that satisfies the average power criterion is outlined.
TL;DR: Novel Bayesian approaches using (uncertain) mixtures of(uncertain numbers of) noise distributions to model data measuring maximum levels of evoked neural responses following various levels of electrical stimulus of nerve tissue are reported on.
Abstract: Neurophysiologists investigating mechanisms underlying neural responses to stimuli have, in recent years, developed substantial interest in modelling certain types of neural response data by using simple mixture distributions. Techniques of mixture deconvolution using likelihood-based techniques have become popular. This paper reports on novel Bayesian approaches using (uncertain) mixtures of (uncertain numbers of) noise distributions to model data measuring maximum levels of evoked neural responses following various levels of electrical stimulus of nerve tissue. We discuss some of the key scientific issues, including physiological hypotheses of 'quantal' levels of neuronal transmissions, together with technical aspects of data analysis, modelling and the use of prior information in addressing these issues within an appropriate Bayesian framework. Illustration of neural response deconvolution analysis using this approach is presented.