Examples in Parametric Inference with R / by Ulhas Jayram Dixit.

Format:

Book

Author/Creator:

Dixit, Ulhas Jayram, Author.

Language:

English

Subjects (All):

Statistics.

Mathematical statistics--Data processing.

Mathematical statistics.

Computer science--Mathematics.

Computer science.

Statistical Theory and Methods.

Statistics and Computing.

Probability and Statistics in Computer Science.

Local Subjects:

Statistical Theory and Methods.

Statistics and Computing.

Probability and Statistics in Computer Science.

Physical Description:

1 online resource (LVIII, 423 p. 26 illus.)

Edition:

1st ed. 2016.

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2016.

Summary:

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.

Contents:

Prerequisite

Chapter 1. Sufficiency and Completeness

Chapter 2. Unbiased Estimation

Chapter 3. Moment and Maximum Likelihood Estimators

Chapter 4. Bound for the Variance

Chapter 5. Consistent Estimator

Chapter 6. Bayes Estimator

Chapter 7. Most Powerful Test

Chapter 8. Unbiased and Other Tests

Bibliography.

Notes:

Includes bibliographical references.

ISBN:

981-10-0889-2

OCLC:

953613210