As the title indicates, this book discusses using the likelihood function for both modeling and inference. It is written as a textbook with a fair number of examples. The author conveniently provides code using the statistical package R for all relevant examples on his web site. He assumes a list of prerequisites that would typically be covered in the first year of a master's degree in statistics (or possibly in a solid undergraduate program in statistics). A good background in probability and theory of statistics, familiarity with applied statistics (such as tests of hypotheses, confidence intervals, least squares and p values), and calculus are prerequisites for using this book. The author presents interesting philosophical discussions in Chapters 1 and 7. In Chapter 1 he explains the differences between a Bayesian versus frequentist approach to statistical inference. He states that the likelihood approach is a compromise between these two approaches and that it could be called a Fisherian approach. He argues that the likelihood approach is non-Bayesian yet has Bayesians aspects and that it has frequentist features but also some nonfrequentist aspects. He references Fisher throughout the book. In Chapter 7 the author discusses the controversial informal likelihood principle, "two datasets (regardless of experimental source) with the same likelihood should lead to the same conclusions." It is hard to be convinced that bow data were collected does not affect conclusions.
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