Springer | Statistical Theory and Methods | June 21, 2016 | ISBN-10: 9811008884 | 529 pages | pdf | 3.97 mb

Authors: Dixit, Ulhas Jayram

Presents sophisticated mathematical proofs in a simple and easy-to-follow language

Discusses fundamental topics common to many fields of statistical inference, and which offer a point of departure for in-depth study

About this Textbook

This book discusses examples in parametric inference with R. Combining basic theory with modern approaches, it presents the latest developments and trends in statistical inference for students who do not have an advanced mathematical and statistical background. The topics discussed in the book are fundamental and common to many fields of statistical inference and thus serve as a point of departure for in-depth study. The book is divided into eight chapters: Chapter 1 provides an overview of topics on sufficiency and completeness, while Chapter 2 briefly discusses unbiased estimation. Chapter 3 focuses on the study of moments and maximum likelihood estimators, and Chapter 4 presents bounds for the variance. In Chapter 5, topics on consistent estimator are discussed. Chapter 6 discusses Bayes, while Chapter 7 studies some more powerful tests. Lastly, Chapter 8 examines unbiased and other tests.

Senior undergraduate and graduate students in statistics and mathematics, and those who have taken an introductory course in probability, will greatly benefit from this book. Students are expected to know matrix algebra, calculus, probability and distribution theory before beginning this course. Presenting a wealth of relevant solved and unsolved problems, the book offers an excellent tool for teachers and instructors who can assign homework problems from the exercises, and students will find the solved examples hugely beneficial in solving the exercise problems.

26 b/w illustrations

Statistical Theory and Methods

Statistics and Computing / Statistics Programs

Probability and Statistics in Computer Science

9789811008887

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