This monograph is written for advanced Master's students, Ph. D. students, and researchers in mathematical statistics and decision theory. It should be useful not only as a basis for graduate courses, seminars, Ph. D. programs, and self-studies, but also as a reference tool. Attheveryleast, readersshouldbefamiliar withbasicconceptscoveredin both advanced undergraduate courses on probability and statistics and int- ductory graduate-level courses on probability theory, mathematical statistics, and analysis. Most statements and proofs appear in a form where standard arguments from measure theory and analysis are su? cient. When additional information is necessary, technical tools, additional measure-theoretic facts, and advanced probabilistic results are presented in condensed form in an - pendix. In particular, topics from measure theory and from the theory of weak convergence of distributions are treated in detail with reference to m- ern books on probability theory, such as Billingsley (1968), Kallenberg (1997, 2002), and Dudley (2002). Building on foundational knowledge, this book acquaints readers with the concepts of classical ? nite sample size decision theory and modern asymptotic decision theory in the sense of LeCam. To this end, systematic applications to the ? elds of parameter estimation, testing hypotheses, and selection of po- lations are included. Some of the problems contain additional information in order to round o? the results, whereas other problems, equipped with so- tions, have a more technical character. The latter play the role of auxiliary results and as such they allow readers to become familiar with the advanced techniques of mathematical statistics.
Inhaltsverzeichnis
Statistical Models. - Tests in Models with Monotonicity Properties. - Statistical Decision Theory. - Comparison of Models, Reduction by. - Invariant Statistical Decision Models. - Large Sample Approximations of Models and Decisions. - Estimation. - Testing. - Selection.