Statistical Modeling and Computation
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Beschreibung
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.
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.
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 Includes supplementary material: sn.pub/extras
Autor*in
Dirk P. Kroese
Themen in »Statistical Modeling and Computation«
Bayesian inference Mathematical statistics Matlab Probability models Statistical inference Statistical modeling
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“Fundamentals of probability and modeling are presented in a rigorous language and the transition to more advanced chapters is almost smooth. Explanations are precise, both verbally and mathematically. Throughout the book, cross-references are made so that the reader can find further or related topics in other parts of the book. Readers interested in mathematical rigor will find this book rewarding. …
Another strength of the book lies in the wealth and variety of exercises at the end of each chapter. The exercises (some with complete solutions) range from mathematical proofs and model building to programming. Solutions for select problems are presented at the end of the book. …
[This] book is outstanding in terms of coverage of topics, rigorous language and integration of computation.” (Abdolvahab Khademi, Journal of Statistical Software, August 2015)
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