Kendall S Advanced Theory Of Statistics Distribution Theory
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Kendall's Advanced Theory of Statistics, Distribution Theory
Author: Maurice George Kendall
language: en
Publisher: Wiley-Interscience
Release Date: 1994-06-30
This major revision contains a largely new chapter 7 providing an extensive discussion of the bivariate and multivariate versions of the standard distributions and families. Chapter 16 has been enlarged to cover multivariate sampling theory, an updated version of material previously found inthe old Volume III. The previous chapters 7 and 8 have been condensed into a single chapter providing an introduction to statistical inference. Elsewhere, major updates include new material on skewness and kurtosis, hazard rate distributions, the bootstrap, the evaluation of the multivariate normalintegral and ratios of quadratic forms. The new edition includes over 200 new references, 40 new exercises and 20 further examples in the main text. In addition, all the text examples have been given titles, and these are listed at the front of the book for easier reference.
Kendall's Advanced Theory of Statistics, Distribution Theory
Kendall's Advanced Theory of Statistics and Kendall's Library of Statistics The development of modern statistical theory is reflected in the history of the late Sir Maurice Kenfall's volumes, The Advanced Theory of Statistics. This landmark publication began life as a two-volume work and grew steadily as a single-authored work until the 1950s. In this edition, there is new material on skewness and kurtosis, hazard rate distribution, the bootstrap, the evaluation of the multivariate normal integral and ratios of quadratic forms. It also includes over 200 new references, 40 new exercises, and 20 further examples in the main text.
All of Statistics
Author: Larry Wasserman
language: en
Publisher: Springer Science & Business Media
Release Date: 2013-12-11
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.