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

FindBook

Google Book

Amazon

博客來

Machine learning for econometrics and related topics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning for econometrics and related topics/ edited by Vladik Kreinovich, Songsak Sriboonchitta, Woraphon Yamaka.
其他作者:	Kreinovich, Vladik.
出版者:	Cham :Springer Nature Switzerland : : 2024.,
面頁冊數:	ix, 499 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Econometrics. -
電子資源:	https://doi.org/10.1007/978-3-031-43601-7
ISBN:	9783031436017

Machine learning for econometrics and related topics
Machine learning for econometrics and related topics[electronic resource] /edited by Vladik Kreinovich, Songsak Sriboonchitta, Woraphon Yamaka. - Cham :Springer Nature Switzerland :2024. - ix, 499 p. :ill. (some col.), digital ;24 cm. - Studies in systems, decision and control,v. 5082198-4190 ;. - Studies in systems, decision and control ;v. 508..

In the last decades, machine learning techniques - especially techniques of deep learning - led to numerous successes in many application areas, including economics. The use of machine learning in economics is the main focus of this book; however, the book also describes the use of more traditional econometric techniques. Applications include practically all major sectors of economics: agriculture, health (including the impact of Covid-19), manufacturing, trade, transportation, etc. Several papers analyze the effect of age, education, and gender on economy - and, more generally, issues of fairness and discrimination. We hope that this volume will: help practitioners to become better knowledgeable of the state-of-the-art econometric techniques, especially techniques of machine learning, and help researchers to further develop these important research directions. We want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments.

ISBN: 9783031436017

Standard No.: 10.1007/978-3-031-43601-7doiSubjects--Topical Terms:

542934
Econometrics.

LC Class. No.: HB139

Dewey Class. No.: 330.015195

Machine learning for econometrics and related topics
LDR:02085nmm a2200325 a 4500 001 2371557
003 DE-He213
005 20240601125443.0
006 m d
007 cr nn 008maaau
008 241127s2024 sz s 0 eng d
020 $a 9783031436017 $q (electronic bk.)
020 $a 9783031436000 $q (paper)
024 7 $a 10.1007/978-3-031-43601-7 $2 doi
035 $a 978-3-031-43601-7
040 $a GP $c GP
041 0 $a eng
050 4 $a HB139
072 7 $a TGB $2 bicssc
072 7 $a TEC009070 $2 bisacsh
072 7 $a TGB $2 thema
082 0 4 $a 330.015195 $2 23
090 $a HB139 $b .M149 2024
245 0 0 $a Machine learning for econometrics and related topics $h [electronic resource] / $c edited by Vladik Kreinovich, Songsak Sriboonchitta, Woraphon Yamaka.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2024.
300 $a ix, 499 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in systems, decision and control, $x 2198-4190 ; $v v. 508
520 $a In the last decades, machine learning techniques - especially techniques of deep learning - led to numerous successes in many application areas, including economics. The use of machine learning in economics is the main focus of this book; however, the book also describes the use of more traditional econometric techniques. Applications include practically all major sectors of economics: agriculture, health (including the impact of Covid-19), manufacturing, trade, transportation, etc. Several papers analyze the effect of age, education, and gender on economy - and, more generally, issues of fairness and discrimination. We hope that this volume will: help practitioners to become better knowledgeable of the state-of-the-art econometric techniques, especially techniques of machine learning, and help researchers to further develop these important research directions. We want to thank all the authors for their contributions and all anonymous referees for their thorough analysis and helpful comments.
650 0 $a Econometrics. $3 542934
650 0 $a Machine learning. $3 533906
650 1 4 $a Mechanical Engineering. $3 891038
650 2 4 $a Economics. $3 517137
650 2 4 $a Mathematical and Computational Engineering Applications. $3 3592737
700 1 $a Kreinovich, Vladik. $3 1965595
700 1 $a Sriboonchitta, Songsak. $3 2056845
700 1 $a Yamaka, Woraphon. $3 3486283
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in systems, decision and control ; $v v. 508. $3 3717658
856 4 0 $u https://doi.org/10.1007/978-3-031-43601-7
950 $a Engineering (SpringerNature-11647)