Volume I of this two-volume text and reference work begins by providing a foundation in measure and integration theory. It then offers a systematic introduction to probability theory, and in particular, those parts that are used in statistics. This volume discusses the law of large numbers for independent and non-independent random variables, transforms, special distributions, convergence in law, the central limit theorem for normal and infinitely divisible laws, conditional expectations and martingales. Unusual topics include the uniqueness and convergence theorem for general transforms with...
Volume I of this two-volume text and reference work begins by providing a foundation in measure and integration theory. It then offers a systematic in...
Part of a two-volume text and reference work, this volume concentrates on the applications of probability to statistics. Topics include: the art of calculating densities of complicated transformations of random vectors; exponential models; consistency of maximum estimators; and other topics.
Part of a two-volume text and reference work, this volume concentrates on the applications of probability to statistics. Topics include: the art of ca...
This book will enable researchers and students of analysis to more easily understand research papers in which probabilistic methods are used to prove theorems of analysis, many of which have no other known proofs. The book assumes a course in measure and integration theory but requires little or no background in probability theory. It emplhasizes topics of interest to analysts, including random series, martingales and Brownian motion.
This book will enable researchers and students of analysis to more easily understand research papers in which probabilistic methods are used to prove ...