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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.

Statistics. --- Mathematical statistics. --- Statistical Theory and Methods. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistics and Computing/Statistics Programs. --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Statistical inference --- Statistics, Mathematical --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Estimation theory. --- Estimating techniques --- Least squares --- Mathematical statistics --- Stochastic processes --- Statistics --- Probabilities --- Sampling (Statistics) --- Econometrics --- Statistics .

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• Reference Manager
• EndNote
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This richly illustrated textbook covers modern statistical methods with applications in medicine, epidemiology and biology. Firstly, it discusses the importance of statistical models in applied quantitative research and the central role of the likelihood function, describing likelihood-based inference from a frequentist viewpoint, and exploring the properties of the maximum likelihood estimate, the score function, the likelihood ratio and the Wald statistic. In the second part of the book, 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. It includes a separate chapter on modern numerical techniques for Bayesian inference, and also addresses advanced topics, such as model choice and prediction from frequentist and Bayesian perspectives. This revised edition of the book “Applied Statistical Inference” has been expanded to include new material on Markov models for time series analysis. It also features a comprehensive appendix covering the prerequisites in probability theory, matrix algebra, mathematical calculus, and numerical analysis, and each chapter is complemented by exercises. The text is primarily intended for graduate statistics and biostatistics students with an interest in applications.

Statistical science --- Biomathematics. Biometry. Biostatistics --- General ecology and biosociology --- biomathematica --- biostatistiek --- statistiek --- ecologie --- Statistics . --- Biostatistics. --- Ecology . --- Biomathematics. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistical Theory and Methods. --- Bayesian Inference. --- Theoretical Ecology/Statistics. --- Genetics and Population Dynamics. --- Probabilitats --- Estadística bayesiana --- Estadística matemàtica --- Biometria --- Estadística mèdica --- Statistics. --- Biometry. --- Ecology. --- Estadística bayesiana.