Seminal Ideas and Controversies in Statistics
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.
Features
- Discusses 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 comparisons
- Provides a deeper understanding and appreciation of the history of statistics
- Discusses disagreements in the literature, which make for interesting reading
- Gives guidance on various aspects of statistics research by reading good examples in the literature
- Promotes the use of good English style in the presentation of statistical ideas, by learning from well-written papers
- Includes an appendix of style tips on writing statistical papers
This 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.
Roderick J. A. Little is Richard D. Remington Distinguished University Professor Emeritus at the University of Michigan, where he also holds emeritus appointments in the Department of Statistics and the Institute for Social Research. After secondary school at Glasgow Academy, he received a B.A. in Mathematics from Gonville and Caius College, Cambridge University, and M.Sc. and Ph.D. degrees in Statistics from the Imperial College of Science and Technology, London University. Professor Little is a pioneer and thought leader in the fields of statistical analysis with missing data, Bayesian inference in sample surveys and causal inference. He has received some of the highest honors in statistics and science, including being elected to the U.S. National Academy of Medicine and American Academy of Arts and Sciences.