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An integrated treatment of statistical inference and computation helps the reader gain a firm understanding of both theory and practice
Discusses modern computation techniques including Markov chain Monte Carlo methods and the Expectation Maximization algorithm
Includes numerous solved examples and exercises
Includes computer codes and a brief programming primer in MATLAB for students
This textbook on statistical modeling and statistical inference will assist advanced undergraduate and graduate students. Statistical Modeling and Computation provides a unique introduction to modern Statistics from both classical and Bayesian perspectives. It also offers an integrated treatment of Mathematical Statistics and modern statistical computation, emphasizing statistical modeling, computational techniques, and applications. Each of the three parts will cover topics essential to university courses. Part I covers the fundamentals of probability theory. In Part II, the authors introduce a wide variety of classical models that include, among others, linear regression and ANOVA models. In Part III, the authors address the statistical analysis and computation of various advanced models, such as generalized linear, state-space and Gaussian models. Particular attention is paid to fast Monte Carlo techniques for Bayesian inference on these models. Throughout the book the authors include a large number of illustrative examples and solved problems. The book also features a section with solutions, an appendix that serves as a MATLAB primer, and a mathematical supplement.
Probability Models.- Random Variables and Probability Distributions.- Joint Distributions.- Common Statistical Models.- Statistical Inference.- Likelihood.- Monte Carlo Sampling.- Bayesian Inference.- Generalized Linear Models.- Dependent Data Models.- State Space Models.- References.- Solutions.- MATLAB Primer.- Mathematical Supplement.- Index.