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Examples in parametric inference with R

Dixit, Ulhas Jayram.

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Examples in parametric inference with R

Record Type:	Electronic resources : Monograph/item
Title/Author:	Examples in parametric inference with R/ by Ulhas Jayram Dixit.
Author:	Dixit, Ulhas Jayram.
Published:	Singapore :Springer Singapore : : 2016.,
Description:	lviii, 423 p. :ill., digital ;24 cm.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Mathematical statistics. -
Online resource:	http://dx.doi.org/10.1007/978-981-10-0889-4
ISBN:	9789811008894

Examples in parametric inference with R
Dixit, Ulhas Jayram.

Examples in parametric inference with R[electronic resource] /by Ulhas Jayram Dixit. - Singapore :Springer Singapore :2016. - lviii, 423 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9789811008894

Standard No.: 10.1007/978-981-10-0889-4doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Examples in parametric inference with R
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245 1 0 $a Examples in parametric inference with R $h [electronic resource] / $c by Ulhas Jayram Dixit.
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505 0 $a 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.
520 $a 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.
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