Seminal Ideas and Controversies in Statistics
Chapman & Hall/CRC (Verlag)
978-1-032-49717-4 (ISBN)
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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, multiple comparisons, and more
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 papers that are well written
Includes an appendix of style tips on writing statistical papers
The 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 will be useful for any researcher 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.
1. Maximum likelihood. 2. To C or not to C-- that is the question. 3. Frequentist flaps: significance testing, hypothesis testing, or something else?. 4. Fiducial inference and the Behrens-Fisher problem. 5. Do you like the likelihood principle?. 6. A Bayesian/frequentist compromise: Calibrated Bayes. 7. Baseball averages, foreign cars, and shrinkage estimation. 8. Alternatives to least squares in regression. 9. Multiple perspectives on multiple comparisons. 10. Generalized Estimating Equations. 11.The Bootstrap and Bayesian Monte-Carlo methods. 12. Exploratory data analysis and data science. 13. Randomization in survey sampling. 14. Randomized clinical trials and the Neyman/Rubin causal model. 15. Propensity score methods.
Erscheint lt. Verlag | 23.4.2025 |
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Reihe/Serie | Chapman & Hall/CRC Monographs on Statistics and Applied Probability |
Zusatzinfo | 7 Tables, black and white; 6 Line drawings, black and white; 6 Illustrations, black and white |
Sprache | englisch |
Maße | 156 x 234 mm |
Themenwelt | Mathematik / Informatik ► Mathematik ► Statistik |
ISBN-10 | 1-032-49717-3 / 1032497173 |
ISBN-13 | 978-1-032-49717-4 / 9781032497174 |
Zustand | Neuware |
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