Lecture notes in statistics ;

The book gives an account of the mathematical-statistical theory of the main classes of parametric statistical models, i.e. transformatioon models and exponential models, and of likelihood based inference. The emphasis is on recent developments - various new results are presented - and the mathematical techniques employed include parts of the theory of group actions and invariant measures, differential geometry, and asymptotic analysis. A knowledge of these techniques is not presupposed but will be helpful, as the exposition is partly quite succinct. A basic knowledge of classic parametric statistical inference is however assumed. Exactness results and high-order asymptotic results for important likelihood quantities, including maximum likelihood estimators, score vectors, (signed) likelihood ratios and (modified) profile likelihoods, are discussed. Concepts of ancillarity and sufficiency enter prominently.

Copulas are functions that join multivariate distribution functions to their one-dimensional margins. In this book, the student or practitioner of statistics and probability will find discussions of the fundamental properties of copulas and some of their primary applications. With nearly 100 examples and over 150 exercises, this book is suitable as a text or for self-study. The only prerequisite is an upper level undergraduate course in probability and mathematical statistics, although some familiarity with nonparametric statistics would be useful. Knowledge of measure-theoretic probability is not required.

From the reviews: "This collection of essays surveys the most important of Fisher's papers in various areas of statistics. ... ... the monograph will be a useful source of reference to most of Fisher's major papers; it will certainly provide background material for much vigorous discussion." #Australian Journal of Statistics#1