Statistical Modeling and Computation

  • Dirk P. Kroese
  • Joshua C.C. Chan

Table of contents

  1. Front Matter
    Pages i-xx
  2. Fundamentals of Probability

    1. Front Matter
      Pages 1-1
    2. Dirk P. Kroese, Joshua C. C. Chan
      Pages 3-21
    3. Dirk P. Kroese, Joshua C. C. Chan
      Pages 23-61
    4. Dirk P. Kroese, Joshua C. C. Chan
      Pages 63-97
  3. Statistical Modeling and Classical and Bayesian Inference

    1. Front Matter
      Pages 99-99
    2. Dirk P. Kroese, Joshua C. C. Chan
      Pages 101-120
    3. Dirk P. Kroese, Joshua C. C. Chan
      Pages 121-159
    4. Dirk P. Kroese, Joshua C. C. Chan
      Pages 161-194
    5. Dirk P. Kroese, Joshua C. C. Chan
      Pages 195-226
    6. Dirk P. Kroese, Joshua C. C. Chan
      Pages 227-262
  4. Advanced Models and Inference

    1. Front Matter
      Pages 263-263
    2. Dirk P. Kroese, Joshua C. C. Chan
      Pages 265-286
    3. Dirk P. Kroese, Joshua C. C. Chan
      Pages 287-322
    4. Dirk P. Kroese, Joshua C. C. Chan
      Pages 323-348
    5. Dirk P. Kroese, Joshua C. C. Chan
      Pages 349-366
    6. Dirk P. Kroese, Joshua C. C. Chan
      Pages 367-372
  5. Back Matter
    Pages 373-400

About this book

Introduction

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.​

Keywords

Bayesian inference Mathematical statistics Matlab Probability models Statistical inference Statistical modeling

Authors and affiliations

  • Dirk P. Kroese
    • 1
  • Joshua C.C. Chan
    • 2
  1. 1.School of Mathematics and PhysicsThe University of QueenslandBrisbaneAustralia
  2. 2.Department of EconomicsAustralian National UniversityCanberraAustralia

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4614-8775-3
  • Copyright Information The Author(s) 2014
  • Publisher Name Springer, New York, NY
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-1-4614-8774-6
  • Online ISBN 978-1-4614-8775-3
  • About this book