Theories of Statistical Inference. The Integrated Bayes/Likelihood Approach. t-Tests and Normal Variance Tests. Unified Analysis of Finite Populations. Regression and Analysis of Variance. Binomial and Multinomial Data. Goodness of Fit and Model Diagnostics. Complex Models. References. Indices.
Murray Aitkin is an honorary professorial fellow in the Department of Mathematics and Statistics at the University of Melbourne in Australia.
This is a stimulating book that should be of interest to Bayesians and statisticians with a general interest in statistical inference. ! It is likely to be controversial, even heretical, to Bayesians. However, this is precisely why it is worth reading: in exploring the new ideas, whether we ultimately accept them or not, we gain a better understanding of the current orthodoxy. ! one of the interesting contributions of the book is the discussion of the use of Bayes factors -- if not 'from the inside,' at least from someone who has been thinking deeply about them for some time. ! The book contains some useful points that are known but ought to be better known, and it is useful to have a reference to them. ! I am pleased to have had the opportunity to read it. --A.H. Welsh, Australian & New Zealand Journal of Statistics, 2011 The emphasis on evidence rather than decision theory makes the book especially relevant to scientific investigations. It gives interesting and thoughtful comparisons to alternative approaches to inference ! The very deep and solid inferential foundations the book lays support a carefully thought out and impressive superstructure, covering topics which include variance component models, finite mixtures, regression, anova, complex survey designs, and other topics. It would provide a valuable and thought-provoking volume for advanced students studying the foundations of inference and their practical implications. It would make a particularly good book for a reading group. --David Hand, International Statistical Review (2011), 79, 1