Book Reviews 27.2
Statistical Modeling and Computation
Dirk Kroese and Joshua Chan
Hardcover: 400 pages
Dirk Kroese (from UQ, Brisbane) and Joshua Chan (from ANU, Canberra) wrote a book intended mostly for an undergraduate audience (or graduate students with no probability or statistics background). Given that prerequisite, Statistical Modeling and Computation is fairly standard in that it recalls probability basics, the principles of statistical inference, and classical parametric models. In a third part, the authors cover “advanced models” such as generalized linear models, time series, and state-space models. The specificity of the book lays in the inclusion of simulation methods, in particular MCMC methods, and illustrations by MATLAB code boxes. (Codes are available on the companion website, along with R translations.) Thus, it has a lot in common with our Bayesian Essentials with R, meaning I am not the most appropriate or least unbiased reviewer for this book.Some content is only viewable by ASA Members. If you are an ASA member, log in to Members Only and look for CHANCE under your publications.