Applied Statistical Inference: Likelihood and Bayes

Leonhard Held · Daniel Sabanés Bové

Nov 2013 · Springer Science & Business Media

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This book covers modern statistical inference based on likelihood with applications in medicine, epidemiology and biology. Two introductory chapters discuss the importance of statistical models in applied quantitative research and the central role of the likelihood function. The rest of the book is divided into three parts. The first describes likelihood-based inference from a frequentist viewpoint. Properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic are discussed in detail. In the second part, likelihood is combined with prior information to perform Bayesian inference. Topics include Bayesian updating, conjugate and reference priors, Bayesian point and interval estimates, Bayesian asymptotics and empirical Bayes methods. Modern numerical techniques for Bayesian inference are described in a separate chapter. Finally two more advanced topics, model choice and prediction, are discussed both from a frequentist and a Bayesian perspective.

A comprehensive appendix covers the necessary prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis.

Autoren-Profil

Leonhard Held is a Professor of Biostatistics at the University of Zurich, Switzerland. He has served as Editor or Associate Editor for Biometrical Journal, Biostatistics and Applied Statistics (JRSSC). He has published several books and numerous articles in statistical methodology, applied statistics and biomedical research. He teaches undergraduate and graduate-level courses in Biostatistics and Medical Statistics.

Daniel Sabanés Bové wrote his PhD thesis in Statistics at the University of Zurich under the supervision of Leonhard Held. He received the Bernd-Streitberg young researcher award from the German Region of the International Biometrical Society.