東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Large sample techniques for statistics

SpringerLink (Online service)

FindBook

Google Book

Amazon

博客來

Large sample techniques for statistics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Large sample techniques for statistics/ by Jiming Jiang.
作者:	Jiang, Jiming.
出版者:	Cham :Springer International Publishing : : 2022.,
面頁冊數:	xv, 685 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Sampling (Statistics) -
電子資源:	https://doi.org/10.1007/978-3-030-91695-4
ISBN:	9783030916954

Large sample techniques for statistics
Jiang, Jiming.

Large sample techniques for statistics[electronic resource] /by Jiming Jiang. - Second edition. - Cham :Springer International Publishing :2022. - xv, 685 p. :ill. (some col.), digital ;24 cm. - Springer texts in statistics,2197-4136. - Springer texts in statistics..

This book offers a comprehensive guide to large sample techniques in statistics. With a focus on developing analytical skills and understanding motivation, Large Sample Techniques for Statistics begins with fundamental techniques, and connects theory and applications in engaging ways. The first five chapters review some of the basic techniques, such as the fundamental epsilon-delta arguments, Taylor expansion, different types of convergence, and inequalities. The next five chapters discuss limit theorems in specific situations of observational data. Each of the first ten chapters contains at least one section of case study. The last six chapters are devoted to special areas of applications. This new edition introduces a final chapter dedicated to random matrix theory, as well as expanded treatment of inequalities and mixed effects models. The book's case studies and applications-oriented chapters demonstrate how to use methods developed from large sample theory in real world situations. The book is supplemented by a large number of exercises, giving readers opportunity to practice what they have learned. Appendices provide context for matrix algebra and mathematical statistics. The Second Edition seeks to address new challenges in data science. This text is intended for a wide audience, ranging from senior undergraduate students to researchers with doctorates. A first course in mathematical statistics and a course in calculus are prerequisites.

ISBN: 9783030916954

Standard No.: 10.1007/978-3-030-91695-4doiSubjects--Topical Terms:

545623
Sampling (Statistics)

LC Class. No.: QA276.6 / .J53 2022

Dewey Class. No.: 519.52

Large sample techniques for statistics
LDR:02572nmm a2200361 a 4500 001 2299156
003 DE-He213
005 20220404122353.0
006 m d
007 cr nn 008maaau
008 230324s2022 sz s 0 eng d
020 $a 9783030916954 $q (electronic bk.)
020 $a 9783030916947 $q (paper)
024 7 $a 10.1007/978-3-030-91695-4 $2 doi
035 $a 978-3-030-91695-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276.6 $b .J53 2022
072 7 $a PBT $2 bicssc
072 7 $a PBWL $2 bicssc
072 7 $a MAT029000 $2 bisacsh
072 7 $a PBT $2 thema
072 7 $a PBWL $2 thema
082 0 4 $a 519.52 $2 23
090 $a QA276.6 $b .J61 2022
100 1 $a Jiang, Jiming. $3 1086534
245 1 0 $a Large sample techniques for statistics $h [electronic resource] / $c by Jiming Jiang.
250 $a Second edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2022.
300 $a xv, 685 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer texts in statistics, $x 2197-4136
520 $a This book offers a comprehensive guide to large sample techniques in statistics. With a focus on developing analytical skills and understanding motivation, Large Sample Techniques for Statistics begins with fundamental techniques, and connects theory and applications in engaging ways. The first five chapters review some of the basic techniques, such as the fundamental epsilon-delta arguments, Taylor expansion, different types of convergence, and inequalities. The next five chapters discuss limit theorems in specific situations of observational data. Each of the first ten chapters contains at least one section of case study. The last six chapters are devoted to special areas of applications. This new edition introduces a final chapter dedicated to random matrix theory, as well as expanded treatment of inequalities and mixed effects models. The book's case studies and applications-oriented chapters demonstrate how to use methods developed from large sample theory in real world situations. The book is supplemented by a large number of exercises, giving readers opportunity to practice what they have learned. Appendices provide context for matrix algebra and mathematical statistics. The Second Edition seeks to address new challenges in data science. This text is intended for a wide audience, ranging from senior undergraduate students to researchers with doctorates. A first course in mathematical statistics and a course in calculus are prerequisites.
650 0 $a Sampling (Statistics) $3 545623
650 1 4 $a Probability Theory. $3 3538789
650 2 4 $a Statistical Theory and Methods. $3 891074
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer texts in statistics. $3 1567152
856 4 0 $u https://doi.org/10.1007/978-3-030-91695-4
950 $a Mathematics and Statistics (SpringerNature-11649)