國立高雄大學圖資館 |

Language: English

Back

Bootstrap methodswith applications i...

Dikta, Gerhard.

Bootstrap methodswith applications in R /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Bootstrap methodsby Gerhard Dikta, Marsel Scheer.
Reminder of title:	with applications in R /
Author:	Dikta, Gerhard.
other author:	Scheer, Marsel.
Published:	Cham :Springer International Publishing :2021.
Description:	xvi, 256 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Bootstrap (Statistics)Data processing.
Online resource:	https://doi.org/10.1007/978-3-030-73480-0
ISBN:	9783030734800

Bootstrap methodswith applications in R /
Dikta, Gerhard.

Bootstrap methodswith applications in R /[electronic resource] :by Gerhard Dikta, Marsel Scheer. - Cham :Springer International Publishing :2021. - xvi, 256 p. :ill., digital ;24 cm.

Introduction -- Generating random numbers -- The classical bootstrap -- Bootstrap based tests -- Regression analysis -- Goodness of fit test for generalized linear models -- boot package -- s i mTool package -- boot GOF package -- Session Info -- Notation and References -- Index.

This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time. The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.

ISBN: 9783030734800

Standard No.: 10.1007/978-3-030-73480-0doiSubjects--Topical Terms:

905469
Bootstrap (Statistics)
--Data processing.

LC Class. No.: QA276.8 / .D55 2021

Dewey Class. No.: 519.54

Bootstrap methodswith applications in R /
LDR:02166nmm a2200325 a 4500 001 608146
003 DE-He213
005 20210811133928.0
006 m d
007 cr nn 008maaau
008 220119s2021 sz s 0 eng d
020 $a 9783030734800 $q (electronic bk.)
020 $a 9783030734794 $q (paper)
024 7 $a 10.1007/978-3-030-73480-0 $2 doi
035 $a 978-3-030-73480-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276.8 $b .D55 2021
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.54 $2 23
090 $a QA276.8 $b .D575 2021
100 1 $a Dikta, Gerhard. $3 905467
245 1 0 $a Bootstrap methods $h [electronic resource] : $b with applications in R / $c by Gerhard Dikta, Marsel Scheer.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a xvi, 256 p. : $b ill., digital ; $c 24 cm.
505 0 $a Introduction -- Generating random numbers -- The classical bootstrap -- Bootstrap based tests -- Regression analysis -- Goodness of fit test for generalized linear models -- boot package -- s i mTool package -- boot GOF package -- Session Info -- Notation and References -- Index.
520 $a This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time. The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.
650 0 $a Bootstrap (Statistics) $x Data processing. $3 905469
650 0 $a R (Computer program language) $3 210846
650 1 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 274067
650 2 4 $a Biostatistics. $3 339693
700 1 $a Scheer, Marsel. $3 905468
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-030-73480-0
950 $a Mathematics and Statistics (SpringerNature-11649)