Roderick J. A. Little

Statistics has developed as a field through seminal ideas and fascinating controversies. Seminal Ideas and Controversies in Statistics concerns a wide-ranging set of 15 important statistical topics, grouped into three general areas: philosophical approaches to statistical inference, important statistical methodology for applications, and topics on statistical design, focusing on the role of randomization. The key papers on each topic are discussed with commentaries to help explain them. The goal is to expand reader knowledge of the statistics literature and encourage a historical perspective. FeaturesDiscusses a number of important ideas in the history of statistics, including the likelihood principle, Bayes vs. frequentist approaches to inference, alternative approaches to least squares regression, shrinkage estimation, hypothesis testing, and multiple comparisonsProvides a deeper understanding and appreciation of the history of statisticsDiscusses disagreements in the literature, which make for interesting readingGives guidance on various aspects of statistics research by reading good examples in the literaturePromotes the use of good English style in the presentation of statistical ideas, by learning from well-written papersIncludes an appendix of style tips on writing statistical papersThis book is aimed at researchers and graduate students in statistics and biostatistics, who are interested in the history of statistics and would like to deepen their understanding of seminal ideas and controversies. It could be used to teach a special topics course or useful for any researchers keen to understand the subject better and improve their statistical presentation skills.

Statistics has developed as a field through seminal ideas and fascinating controversies. Seminal Ideas and Controversies in Statistics concerns a wide-ranging set of 15 important statistical topics, grouped into three general areas: philosophical approaches to statistical inference, important statistical methodology for applications, and topics on statistical design, focusing on the role of randomization. The key papers on each topic are discussed with commentaries to help explain them. The goal is to expand reader knowledge of the statistics literature and encourage a historical perspective. FeaturesDiscusses a number of important ideas in the history of statistics, including the likelihood principle, Bayes vs. frequentist approaches to inference, alternative approaches to least squares regression, shrinkage estimation, hypothesis testing, and multiple comparisonsProvides a deeper understanding and appreciation of the history of statisticsDiscusses disagreements in the literature, which make for interesting readingGives guidance on various aspects of statistics research by reading good examples in the literaturePromotes the use of good English style in the presentation of statistical ideas, by learning from well-written papersIncludes an appendix of style tips on writing statistical papersThis book is aimed at researchers and graduate students in statistics and biostatistics, who are interested in the history of statistics and would like to deepen their understanding of seminal ideas and controversies. It could be used to teach a special topics course or useful for any researchers keen to understand the subject better and improve their statistical presentation skills.

An up-to-date, comprehensive treatment of a classic text on missing data in statisticsThe topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism, and then they apply the theory to a wide range of important missing data problems. Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated “classic” written by renowned authorities on the subjectFeatures over 150 exercises (including many new ones)Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methodsRevises previous topics based on past student feedback and class experienceContains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work "has been no less than defining and transforming." (ISI)Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.