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Bayesian inference provides a simple and unified approach to data analysis, allowing experimenters to assign probabilities to competing hypotheses of interest, on the basis of the current state of knowledge. By incorporating relevant prior information, it can sometimes improve model parameter estimates by many orders of magnitude. This book provides a clear exposition of the underlying concepts with many worked examples and problem sets. It also discusses implementation, including an introduction to Markov chain Monte-Carlo integration and linear and nonlinear model fitting. Particularly extensive coverage of spectral analysis (detecting and measuring periodic signals) includes a self-contained introduction to Fourier and discrete Fourier methods. There is a chapter devoted to Bayesian inference with Poisson sampling, and three chapters on frequentist methods help to bridge the gap between the frequentist and Bayesian approaches. Supporting Mathematica® notebooks with solutions to selected problems, additional worked examples, and a Mathematica tutorial are available at www.cambridge.org/9780521150125.

Bayesian statistical decision theory. --- Physical sciences --- Statistische data-analyse. --- Statistical methods. --- Mathematica (Computer file). --- Science --- Bayes' solution --- Bayesian analysis --- Statistical decision --- Mathematica (Computer file)

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Longitudinal data are ubiquitous across Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education, yet many longitudinal data sets remain improperly analyzed. This book teaches the art and statistical science of modern longitudinal data analysis. The author emphasizes specifying, understanding, and interpreting longitudinal data models. He inspects the longitudinal data graphically, analyzes the time trend and covariates, models the covariance matrix, and then draws conclusions. Covariance models covered include random effects, autoregressive, autoregressive moving average, antedependence, factor analytic, and completely unstructured models among others. Longer expositions explore: an introduction to and critique of simple non-longitudinal analyses of longitudinal data, missing data concepts, diagnostics, and simultaneous modeling of two longitudinal variables. Applications and issues for random effects models cover estimation, shrinkage, clustered data, models for binary and count data and residuals and residual plots. Shorter sections include a general discussion of how computational algorithms work, handling transformed data, and basic design issues. This book requires a solid regression course as background and is particularly intended for the final year of a Biostatistics or Statistics Masters degree curriculum. The mathematical prerequisite is generally low, mainly assuming familiarity with regression analysis in matrix form. Doctoral students in Biostatistics or Statistics, applied researchers and quantitative doctoral students in disciplines such as Medicine, Public Health, Public Policy, Psychology, Political Science, Biology, Sociology and Education will find this book invaluable. The book has many figures and tables illustrating longitudinal data and numerous homework problems. The associated web site contains many longitudinal data sets, examples of computer code, and labs to re-enforce the material. Robert Weiss is Professor of Biostatistics in the UCLA School of Public Health with a Ph.D. in Statistics from the University of Minnesota. He is expert in longitudinal data analysis, diagnostics and graphics, and Bayesian methods, and specializes in modeling of hierarchical and complex data sets. He has published over 50 papers a majority of which involves longitudinal data. He regularly teaches classes in longitudinal data analysis, multivariate analysis, Bayesian inference, and statistical graphics.

Data-analyse. --- Longitudinaal onderzoek. --- Longitudinal method. --- Mathematical statistics. --- Mathematical statistics --- Epidemiology. --- Statistics. --- Science, Humanities and Social Sciences, multidisciplinary. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistical Theory and Methods. --- Statistics for Social Sciences, Humanities, Law. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Mathematics --- Econometrics --- Diseases --- Public health --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Statistics . --- Longitudinal method --- Data-analyse --- Longitudinaal onderzoek --- Longitudinal research --- Longitudinal studies --- Methodology --- Research --- Social sciences

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This publication provides all the information required to understand the PISA 2003 educational performance database and perform analyses in accordance with the complex methodologies used to collect and process the data. It enables researchers to both reproduce the initial results and to undertake further analyses. The publication includes introductory chapters explaining the statistical theories and concepts required to analyse the PISA data, including full chapters on how to apply replicate weights and undertake analyses using plausible values; worked examples providing full syntax in SAS®; and a comprehensive description of the OECD PISA 2003 international database.   The PISA 2003 database includes micro-level data on student educational performance for 41 countries collected in 2003, together with students’ responses to the PISA 2003 questionnaires and the test questions. A similar manual is available for SPSS users.

Educational tests and measurements --- Educational evaluation --- Tests et mesures en éducation --- Evaluation en éducation --- Programme for International Student Assessment. --- Mediciones y pruebas educativas --- Evaluacion educativa --- Évaluation en éducation --- Éducation --- Tests et mesures en education --- Evaluation en education --- Education --- Statistiques --- Enquetes --- Programme international pour le suivi des acquis des eleves. --- SAS (Logiciel) --- Educational evaluation -- OECD countries. --- Educational tests and measurements -- OECD countries. --- Social Sciences --- Theory & Practice of Education --- Evaluación educativa --- Éducation --- Évaluation en éducation --- Data-analyse. --- Internationaal onderwijs. --- Studieresultaten. --- Enquêtes --- Enquêtes --- Programme international pour le suivi des acquis des élèves. --- Tests et mesures en éducation --- Evaluation en éducation --- Évaluation --- Educational assessment --- Educational program evaluation --- Evaluation research in education --- Instructional systems analysis --- Program evaluation in education --- Self-evaluation in education --- Evaluation --- Educational measurements --- Mental tests --- Tests and measurements in education --- Psychological tests for children --- Psychometrics --- Students --- Examinations --- Psychological tests --- Rating of --- Organisation for Economic Co-operation and Development. --- International Programme for Student Assessment --- OECD/PISA --- PISA --- Programa Internacional de Avaliação de Alunos --- Program for International Student Assessment --- Starptautiskā skolēnu novērtēšanas programma --- Programme international pour le suivi des acquis des élèves --- Programa Internacional de Evaluación de Estudiantes