Statistics has developed as a field through seminal ideas and fascinating controversies. This book concerns a wide-ranging set of 13 important statistical topics, grouped into three general areas:
Statistics has developed as a field through seminal ideas and fascinating controversies. This book concerns a wide-ranging set of 13 important statistical topics, grouped into three general areas:Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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.
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.
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