國立高雄大學圖資館 |

登入

回首頁

Fundamentals of high-dimensional statisticswith exercises and R labs /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Fundamentals of high-dimensional statisticsby Johannes Lederer.
其他題名:	with exercises and R labs /
作者:	Lederer, Johannes.
出版者:	Cham :Springer International Publishing :2022.
面頁冊數:	xiv, 355 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Mathematical statistics.
電子資源:	https://doi.org/10.1007/978-3-030-73792-4
ISBN:	9783030737924$q(electronic bk.)

Fundamentals of high-dimensional statisticswith exercises and R labs /
Lederer, Johannes.

Fundamentals of high-dimensional statisticswith exercises and R labs /[electronic resource] :by Johannes Lederer. - Cham :Springer International Publishing :2022. - xiv, 355 p. :ill., digital ;24 cm. - Springer texts in statistics,2197-4136. - Springer texts in statistics..

Preface -- Notation -- Introduction -- Linear Regression -- Graphical Models -- Tuning-Parameter Calibration -- Inference -- Theory I: Prediction -- Theory II: Estimation and Support Recovery -- A Solutions -- B Mathematical Background -- Bibliography -- Index.

This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that convey valuable practical insights. The book covers the theory and practice of high-dimensional linear regression, graphical models, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate and advanced undergraduate students in statistics, biostatistics, and bioinformatics, though it will be equally useful to a broader audience.

ISBN: 9783030737924$q(electronic bk.)

Standard No.: 10.1007/978-3-030-73792-4doiSubjects--Topical Terms:

181877
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Fundamentals of high-dimensional statisticswith exercises and R labs /
LDR:01987nmm a2200337 a 4500 001 618326
003 DE-He213
005 20211116152307.0
006 m d
007 cr nn 008maaau
008 221025s2022 sz s 0 eng d
020 $a 9783030737924$q(electronic bk.)
020 $a 9783030737917$q(paper)
024 7 $a 10.1007/978-3-030-73792-4 $2 doi
035 $a 978-3-030-73792-4
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
072 7 $a PBT $2 thema
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .L473 2022
100 1 $a Lederer, Johannes. $3 917937
245 1 0 $a Fundamentals of high-dimensional statistics $h [electronic resource] : $b with exercises and R labs / $c by Johannes Lederer.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xiv, 355 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer texts in statistics, $x 2197-4136
505 0 $a Preface -- Notation -- Introduction -- Linear Regression -- Graphical Models -- Tuning-Parameter Calibration -- Inference -- Theory I: Prediction -- Theory II: Estimation and Support Recovery -- A Solutions -- B Mathematical Background -- Bibliography -- Index.
520 $a This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that convey valuable practical insights. The book covers the theory and practice of high-dimensional linear regression, graphical models, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate and advanced undergraduate students in statistics, biostatistics, and bioinformatics, though it will be equally useful to a broader audience.
650 0 $a Mathematical statistics. $3 181877
650 1 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Big Data. $3 760530
650 2 4 $a Data Structures and Information Theory. $3 825714
650 2 4 $a Artificial Intelligence. $3 212515
650 2 4 $a Statistics and Computing/Statistics Programs. $3 275710
650 2 4 $a Machine Learning. $3 833608
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
830 0 $a Springer texts in statistics. $3 559235
856 4 0 $u https://doi.org/10.1007/978-3-030-73792-4
950 $a Mathematics and Statistics (SpringerNature-11649)