Wiley series in probability and mathematical statistics.

Introduces the philosophy of experimentation and the part that statistics play in experimentation. Emphasizes the need to develop a capability for "statistical thinking" by using examples drawn from actual case studies.

Vol. 2 has series: Wiley series in probability and mathematical statistics. Bibliographical footnotes. "Some books on cagnate subjects": v. 2, p. 615-616.

From its initial publication this book has been the standard text on the subject. Since then, there has been a continuing high level of activity, and work has developed in all major areas, including univariate and multivariate data, linear models, regression, and time series. Thus a third edition was obviously essential. Every essential area is thoroughly updated to reflect the latest state of knowledge. All the topics are fully revised and extended, and additional topics and new emphases are presented. Important features of this new edition are comprehensive updating of methodology and tables; major expansion of topics including time series, contingency tables, multivariate analysis and regression data (particularly influence and robustness, diagnostics, multiple outliers, non-linear models, L[subscript 1]-norm methods); and richer coverage of practical fields and computer-based facilities, and a thoroughly updated reference list. New introduced topics include standards and regulations, computer software packages, and sample surveys. To facilitate ease of use, the book has been restructured into four parts: Basic Principles, Univariate Data, Multivariate and Structured Data, and Special Topics. This new edition, with the revised and new material, is sure to enhance the book's recognised position as the reference on the subject of outliers.

Drawing on methodology from several areas of probability theory and mathematics, this monograph develops the mathematical theory of models for random coverage patterns, introduces the concepts underlying their generation and illustrates their applications for a variety of fields.

An up-to-date and concise description of recent results in probability theory and stochastic processes useful in the study of asymptotic theory of statistical inference. Brings together new material on the interplay between recent advances in probability theory and their applications to the asymptotic theory of statistical inference. Asymptotic theory of maximum likelihood and Bayes estimation, asymptotic properties of least squares estimators in nonlinear regression, and estimators of parameters for stable laws are dicussed from the point of view of stochastic processes. This leads to better results than the Taylor expansions approach used in the classical theory of maximum likelihood estimation.