國立高雄大學圖資館 |

Language: English

Back

Examples in parametric inference with R

Dixit, Ulhas Jayram.

Examples in parametric inference with R

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

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$q(electronic bk.)

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

181877
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Examples in parametric inference with R
LDR:02738nmm a2200313 a 4500 001 489441
003 DE-He213
005 20161102104447.0
006 m d
007 cr nn 008maaau
008 161213s2016 si s 0 eng d
020 $a 9789811008894$q(electronic bk.)
020 $a 9789811008887$q(paper)
024 7 $a 10.1007/978-981-10-0889-4 $2 doi
035 $a 978-981-10-0889-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .D619 2016
100 1 $a Dixit, Ulhas Jayram. $3 747940
245 1 0 $a Examples in parametric inference with R $h [electronic resource] / $c by Ulhas Jayram Dixit.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2016.
300 $a lviii, 423 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Mathematical statistics. $3 181877
650 0 $a R (Computer program language) $3 210846
650 1 4 $a Statistics. $3 182057
650 2 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Statistics and Computing/Statistics Programs. $3 275710
650 2 4 $a Probability and Statistics in Computer Science. $3 274053
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-981-10-0889-4
950 $a Mathematics and Statistics (Springer-11649)