國立虎尾科技大學 |

Bootstrap Methods = With Applications in R /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Bootstrap Methods/ by Gerhard Dikta, Marsel Scheer.
Reminder of title:	With Applications in R /
Author:	Dikta, Gerhard.
other author:	Scheer, Marsel.
Description:	XVI, 256 p. 36 illus., 29 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Statistics . -
Online resource:	https://doi.org/10.1007/978-3-030-73480-0
ISBN:	9783030734800

Bootstrap Methods = With Applications in R /
Dikta, Gerhard.

Bootstrap MethodsWith Applications in R /[electronic resource] :by Gerhard Dikta, Marsel Scheer. - 1st ed. 2021. - XVI, 256 p. 36 illus., 29 illus. in color.online resource.

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:

1253516
Statistics .

LC Class. No.: QA276-280

Dewey Class. No.: 519.5

Bootstrap Methods = With Applications in R /
LDR:02545nam a22003975i 4500 001 1053965
003 DE-He213
005 20210811133928.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030734800 $9 978-3-030-73480-0
024 7 $a 10.1007/978-3-030-73480-0 $2 doi
035 $a 978-3-030-73480-0
050 4 $a QA276-280
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
100 1 $a Dikta, Gerhard. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1358920
245 1 0 $a Bootstrap Methods $h [electronic resource] : $b With Applications in R / $c by Gerhard Dikta, Marsel Scheer.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XVI, 256 p. 36 illus., 29 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
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 Statistics . $3 1253516
650 0 $a Biostatistics. $3 783654
650 1 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 670172
700 1 $a Scheer, Marsel. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1358921
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030734794
776 0 8 $i Printed edition: $z 9783030734817
776 0 8 $i Printed edition: $z 9783030734824
856 4 0 $u https://doi.org/10.1007/978-3-030-73480-0
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)