國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁 到查詢結果 [ author_sort:"berk, richard a." ]

切換: 標籤 | MARC模式 | ISBD

Statistical learning from a regressi...

Berk, Richard A.

Statistical learning from a regression perspective

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Statistical learning from a regression perspectiveby Richard A. Berk.
作者:	Berk, Richard A.
出版者:	Cham :Springer International Publishing :2020.
面頁冊數:	xxvi, 433 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Statistics.
電子資源:	https://doi.org/10.1007/978-3-030-40189-4
ISBN:	9783030401894$q(electronic bk.)

Statistical learning from a regression perspective
Berk, Richard A.

Statistical learning from a regression perspective[electronic resource] /by Richard A. Berk. - Third edition. - Cham :Springer International Publishing :2020. - xxvi, 433 p. :ill., digital ;24 cm. - Springer texts in statistics,1431-875X. - Springer texts in statistics..

Preface -- Preface To Second Edition -- Preface To Third Edition -- 1 Statistical Learning as a Regression Problem -- 2 Splines, Smoothers, and Kernels -- 3 Classification and Regression Trees (CART) -- 4 Bagging -- 5 Random Forests -- 6 Boosting -- 7 Support Vector Machines -- 8 Neural Networks -- 9 Reinforcement Learning and Genetic Algorithms -- 10 Integration Themes and a Bit of Craft Lore -- Index.

This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised learning properly can be seen as a form of regression analysis. Key concepts and procedures are illustrated with a large number of real applications and their associated code in R, with an eye toward practical implications. The growing integration of computer science and statistics is well represented including the occasional, but salient, tensions that result. Throughout, there are links to the big picture. The third edition considers significant advances in recent years, among which are: the development of overarching, conceptual frameworks for statistical learning; the impact of "big data" on statistical learning; the nature and consequences of post-model selection statistical inference; deep learning in various forms; the special challenges to statistical inference posed by statistical learning; the fundamental connections between data collection and data analysis; interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy. This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research. Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems.

ISBN: 9783030401894$q(electronic bk.)

Standard No.: 10.1007/978-3-030-40189-4doiSubjects--Topical Terms:

182057
Statistics.

LC Class. No.: QA278.2 / .B47 2020

Dewey Class. No.: 519.5

Statistical learning from a regression perspective
LDR:03759nmm a2200349 a 4500 001 593303
003 DE-He213
005 20200701225655.0
006 m d
007 cr nn 008maaau
008 210727s2020 sz s 0 eng d
020 $a 9783030401894$q(electronic bk.)
020 $a 9783030401887$q(paper)
024 7 $a 10.1007/978-3-030-40189-4 $2 doi
035 $a 978-3-030-40189-4
040 $a GP $c GP
041 0 $a eng
050 4 $a QA278.2 $b .B47 2020
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 QA278.2 $b .B512 2020
100 1 $a Berk, Richard A. $3 240101
245 1 0 $a Statistical learning from a regression perspective $h [electronic resource] / $c by Richard A. Berk.
250 $a Third edition.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a xxvi, 433 p. : $b ill., digital ; $c 24 cm.
490 1 $a Springer texts in statistics, $x 1431-875X
505 0 $a Preface -- Preface To Second Edition -- Preface To Third Edition -- 1 Statistical Learning as a Regression Problem -- 2 Splines, Smoothers, and Kernels -- 3 Classification and Regression Trees (CART) -- 4 Bagging -- 5 Random Forests -- 6 Boosting -- 7 Support Vector Machines -- 8 Neural Networks -- 9 Reinforcement Learning and Genetic Algorithms -- 10 Integration Themes and a Bit of Craft Lore -- Index.
520 $a This textbook considers statistical learning applications when interest centers on the conditional distribution of a response variable, given a set of predictors, and in the absence of a credible model that can be specified before the data analysis begins. Consistent with modern data analytics, it emphasizes that a proper statistical learning data analysis depends in an integrated fashion on sound data collection, intelligent data management, appropriate statistical procedures, and an accessible interpretation of results. The unifying theme is that supervised learning properly can be seen as a form of regression analysis. Key concepts and procedures are illustrated with a large number of real applications and their associated code in R, with an eye toward practical implications. The growing integration of computer science and statistics is well represented including the occasional, but salient, tensions that result. Throughout, there are links to the big picture. The third edition considers significant advances in recent years, among which are: the development of overarching, conceptual frameworks for statistical learning; the impact of "big data" on statistical learning; the nature and consequences of post-model selection statistical inference; deep learning in various forms; the special challenges to statistical inference posed by statistical learning; the fundamental connections between data collection and data analysis; interdisciplinary ethical and political issues surrounding the application of algorithmic methods in a wide variety of fields, each linked to concerns about transparency, fairness, and accuracy. This edition features new sections on accuracy, transparency, and fairness, as well as a new chapter on deep learning. Precursors to deep learning get an expanded treatment. The connections between fitting and forecasting are considered in greater depth. Discussion of the estimation targets for algorithmic methods is revised and expanded throughout to reflect the latest research. Resampling procedures are emphasized. The material is written for upper undergraduate and graduate students in the social, psychological and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems.
650 0 $a Statistics. $3 182057
650 0 $a Probabilities. $3 182046
650 0 $a Public health. $3 209444
650 0 $a Psychology $x Methodology. $3 325143
650 0 $a Psychometrics. $3 182715
650 0 $a Social sciences. $3 174709
650 1 4 $a Statistical Theory and Methods. $3 274054
650 2 4 $a Probability Theory and Stochastic Processes. $3 274061
650 2 4 $a Statistics for Social Sciences, Humanities, Law. $3 825904
650 2 4 $a Public Health. $3 320661
650 2 4 $a Psychological Methods/Evaluation. $3 275095
650 2 4 $a Methodology of the Social Sciences. $3 275266
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/10.1007/978-3-030-40189-4
950 $a Mathematics and Statistics (SpringerNature-11649)