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

A guide to robust statistical methods

Wilcox, Rand R.

FindBook

Google Book

Amazon

博客來

A guide to robust statistical methods

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	A guide to robust statistical methods/ by Rand R. Wilcox.
作者:	Wilcox, Rand R.
出版者:	Cham :Springer Nature Switzerland : : 2023.,
面頁冊數:	xvii, 326 p. :ill., digital ;24 cm.
內容註:	1. Introduction -- 2. The one-sample case -- 3. Comparing two independent groups -- 4. Comparing two dependent groups -- 5. Comparing multiple independent groups -- 6. Comparing multiple dependent groups -- 7. Robust regression estimators -- 8. Inferential methods based on robust regression estimators -- 9. Measures of association -- 10. Comparing groups when there is a covariate.
Contained By:	Springer Nature eBook
標題:	Mathematical statistics. -
電子資源:	https://doi.org/10.1007/978-3-031-41713-9
ISBN:	9783031417139

A guide to robust statistical methods
Wilcox, Rand R.

A guide to robust statistical methods[electronic resource] /by Rand R. Wilcox. - Cham :Springer Nature Switzerland :2023. - xvii, 326 p. :ill., digital ;24 cm.

1. Introduction -- 2. The one-sample case -- 3. Comparing two independent groups -- 4. Comparing two dependent groups -- 5. Comparing multiple independent groups -- 6. Comparing multiple dependent groups -- 7. Robust regression estimators -- 8. Inferential methods based on robust regression estimators -- 9. Measures of association -- 10. Comparing groups when there is a covariate.

Robust statistical methods are now being used in a wide range of disciplines. The appeal of these methods is that they are designed to perform about as well as classic techniques when standard assumptions are true-but they continue to perform well in situations where classic methods perform poorly. This book provides a relatively non-technical guide to modern methods. The focus is on applying modern methods using R, understanding when and why classic methods can be unsatisfactory, and fostering a conceptual understanding of the relative merits of different techniques. A recurring theme is that no single method reveals everything one would like to know about the population under study. An appeal of robust methods is that under general conditions they provide much higher power than conventional techniques. Perhaps more importantly, they help provide a deeper and more nuanced understanding of data. The book is for readers who had at least one semester of statistics, aimed at non-statisticians.

ISBN: 9783031417139

Standard No.: 10.1007/978-3-031-41713-9doiSubjects--Topical Terms:

516858
Mathematical statistics.

LC Class. No.: QA276 / .W55 2023

Dewey Class. No.: 519.5

A guide to robust statistical methods
LDR:02350nmm a2200325 a 4500 001 2335593
003 DE-He213
005 20231025133459.0
006 m d
007 cr nn 008maaau
008 240402s2023 sz s 0 eng d
020 $a 9783031417139 $q (electronic bk.)
020 $a 9783031417122 $q (paper)
024 7 $a 10.1007/978-3-031-41713-9 $2 doi
035 $a 978-3-031-41713-9
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276 $b .W55 2023
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 .W667 2023
100 1 $a Wilcox, Rand R. $3 740929
245 1 2 $a A guide to robust statistical methods $h [electronic resource] / $c by Rand R. Wilcox.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2023.
300 $a xvii, 326 p. : $b ill., digital ; $c 24 cm.
505 0 $a 1. Introduction -- 2. The one-sample case -- 3. Comparing two independent groups -- 4. Comparing two dependent groups -- 5. Comparing multiple independent groups -- 6. Comparing multiple dependent groups -- 7. Robust regression estimators -- 8. Inferential methods based on robust regression estimators -- 9. Measures of association -- 10. Comparing groups when there is a covariate.
520 $a Robust statistical methods are now being used in a wide range of disciplines. The appeal of these methods is that they are designed to perform about as well as classic techniques when standard assumptions are true-but they continue to perform well in situations where classic methods perform poorly. This book provides a relatively non-technical guide to modern methods. The focus is on applying modern methods using R, understanding when and why classic methods can be unsatisfactory, and fostering a conceptual understanding of the relative merits of different techniques. A recurring theme is that no single method reveals everything one would like to know about the population under study. An appeal of robust methods is that under general conditions they provide much higher power than conventional techniques. Perhaps more importantly, they help provide a deeper and more nuanced understanding of data. The book is for readers who had at least one semester of statistics, aimed at non-statisticians.
650 0 $a Mathematical statistics. $3 516858
650 1 4 $a Statistical Theory and Methods. $3 891074
650 2 4 $a Applied Statistics. $3 3300946
650 2 4 $a Methodology of Data Collection and Processing. $3 3598081
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-41713-9
950 $a Mathematics and Statistics (SpringerNature-11649)