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Statistical Modeling and Computation

Springer New York,
64,19 € Lieferbar in 2-3 Tagen
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This book provides an introduction to modern statistics. It also offers an integrated treatment of mathematical statistics and statistical computation, emphasizing statistical modeling, computational techniques, and applications.


Titel: Statistical Modeling and Computation
Autoren/Herausgeber: Dirk P. Kroese, Joshua C.C. Chan
Ausgabe: Softcover reprint of the original 1st ed. 2014

ISBN/EAN: 9781493953325

Seitenzahl: 400
Format: 23,5 x 15,5 cm
Produktform: Taschenbuch/Softcover
Gewicht: 640 g
Sprache: Englisch

Dirk P. Kroese is a Professor of Mathematics and Statistics at The University of Queensland. He is fascinated by anything that deals with the theory and application of randomness. He has written over 90 publications in a wide range of areas in probability and statistics,  including three influential books: The Cross-Entropy Method and Simulation and the Monte Carlo Method, Second Edition, both with Reuven Rubinstein, and Handbook of Monte Carlo Methods, with Thomas Taimre and Zdravko Botev. Joshua Chan is a Senior Lecturer at the Research School of Economics, Australian National University. His current research focuses on detecting and modeling time-varying structures in macroeconomic data using simulation-based methods. He has published widely in leading international journals such as Journal of Econometrics, Journal of Business and Economic Statistics, and Journal of Computational and Graphical Statistics.

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.

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