This two-volume work on statistical theory is meant to acquaint the reader with the mainstream of present day statistical thinking. Te mathematical prerequisites are a knowledge of undergraduate calculus and an introduction to matrix algebra. While the aim of the authors is mainly to provide the reader with a systematic treatment of statistical theory student at both the undergraduate and post-graduate levels find the book suitable for major parts of their syllabi. The volume of the book is devoted to the broad area of statistical inference and is thus the natural complement of the first, which is concerned with probability distributions. It starts with the concepts of sufficiency and completeness. The various forms which the problem of statistical inference may assume [viz point estimation, hypothesis testing and interval estimation] and the various set-ups under which it may be attacked (including the sequential, the multivariate, the non-parametric and the large-sample) are then taken up one by one. The main principles of description theory, that provides a unified treatment of statistical inference, are also presented. The two volumes jointly serve to acquaint the reader with what may be called the mainstream of present-day statistical thinking. Although primarily designed to meet this needs of the general reader having the requisite mathematical equipment (viz. A knowledge of undergraduate calculus and introductory matrix algebra), the book should also prove useful to undergraduate Honours and post-graduate students of statistics for major parts of their syllabi. The t... See more
This two-volume work on statistical theory is meant to acquaint the reader with the mainstream of present day statistical thinking. Te mathematical prerequisites are a knowledge of undergraduate calculus and an introduction to matrix algebra. While the aim of the authors is mainly to provide the reader with a systematic treatment of statistical theory student at both the undergraduate and post-graduate levels find the book suitable for major parts of their syllabi. The volume of the book is devoted to the broad area of statistical inference and is thus the natural complement of the first, which is concerned with probability distributions. It starts with the concepts of sufficiency and completeness. The various forms which the problem of statistical inference may assume [viz point estimation, hypothesis testing and interval estimation] and the various set-ups under which it may be attacked (including the sequential, the multivariate, the non-parametric and the large-sample) are then taken up one by one. The main principles of description theory, that provides a unified treatment of statistical inference, are also presented. The two volumes jointly serve to acquaint the reader with what may be called the mainstream of present-day statistical thinking. Although primarily designed to meet this needs of the general reader having the requisite mathematical equipment (viz. A knowledge of undergraduate calculus and introductory matrix algebra), the book should also prove useful to undergraduate Honours and post-graduate students of statistics for major parts of their syllabi. The treatment is rather exhaustive and sophisticated throughout! In the new edition, Volume Two has undergone a fairly dairly through revision. More exmaples and exercises have been included. For the benefit of students and teachers alike, we have now included answers of hints to solutions of some of
