소장자료
LDR | 03242cam a2200000 a | ||
001 | 0100728668▲ | ||
005 | 20231106114213▲ | ||
007 | ta ▲ | ||
008 | 211024s2022 nju b 001 0 eng c▲ | ||
010 | ▼a 2021042428▲ | ||
020 | ▼a9781119625391▼q(hbk.)▲ | ||
020 | ▼z9781119625414▼q(pdf)▲ | ||
020 | ▼z9781119625421▼q(epub)▲ | ||
020 | ▼z9781119625438▼q(ebk.)▲ | ||
035 | ▼a(KERIS)REF000019744024▲ | ||
040 | ▼aLBSOR/DLC▼beng▼cDLC▼d221016▲ | ||
082 | 0 | 4 | ▼a519.5/36▼223/eng/20211105▲ |
090 | ▼a519.536▼bS163r▲ | ||
100 | 1 | ▼aSaleh, A. K. Md. Ehsanes.▲ | |
245 | 1 | 0 | ▼aRank-based methods for shrinkage and selection :▼bwith application to machine learning /▼cA.K. Md. Ehsanes Saleh, Mohammad Arashi, Resve A. Saleh, Mina Norouzirad.▲ |
260 | ▼aHoboken, NJ :▼bJohn Wiley & Sons, Inc.,▼c2022.▲ | ||
300 | ▼axxxi, 448 p. ;▼c24 cm▲ | ||
504 | ▼aIncludes bibliographical references and index.▲ | ||
505 | 0 | ▼aIntroduction to rank-based regression -- Characteristics of rank-based penalty estimators -- Location and simple linear models -- Analysis of variance (ANOVA) -- Seemingly unrelated simple linear models -- Multiple linear regression models -- Partially linear multiple regression model -- Liu regression models -- Autoregressive models -- High-dimensional models -- Rank-based logistic regression -- Rank-based neural networks.▲ | |
520 | ▼a"The purpose of this book is to lay the groundwork for robust data science using rankbased methods. The field of machine learning has not yet fully embraced a class of robust estimators that would address issues that limit the value of least-squares estimation. For example, outliers in data sets may produce misleading results that are not suitable for inference. They can also affect results obtained from penalty estimators. We believe that robust estimators for regression problems are well-suited to data science. This book is intended to provide both practical and mathematical foundations in the study of rank-based methods. It will introduce a number of new ideas and approaches to the practice and theory of robust estimation and encourage readers to pursue further investigation in this field. While the main goal of this book is to provide a rigorous treatment of the subject matter, we begin with some introductory material to build insight and intuition about rank-based regression and penalty estimators, especially for those who are new to the topic and those looking to understand key concepts. To motivate the need for such methods, we will start with a discussion of the median as it is the key to rank-based methods and then build on that concept towards the notion of robust data science"--▼cProvided by publisher.▲ | ||
650 | 0 | ▼aRegression analysis.▲ | |
650 | 0 | ▼aBig data.▲ | |
650 | 0 | ▼aMachine learning.▲ | |
700 | 1 | ▼aArashi, Mohammad,▼d1981-▲ | |
700 | 1 | ▼aSaleh, Resve A.,▼d1957-▲ | |
700 | 1 | ▼aNorouzirad, Mina.▲ |
Rank-based methods for shrinkage and selection : with application to machine learning
자료유형
국외단행본
서명/책임사항
Rank-based methods for shrinkage and selection : with application to machine learning / A.K. Md. Ehsanes Saleh, Mohammad Arashi, Resve A. Saleh, Mina Norouzirad.
발행사항
Hoboken, NJ : John Wiley & Sons, Inc. , 2022.
형태사항
xxxi, 448 p. ; 24 cm
서지주기
Includes bibliographical references and index.
내용주기
Introduction to rank-based regression -- Characteristics of rank-based penalty estimators -- Location and simple linear models -- Analysis of variance (ANOVA) -- Seemingly unrelated simple linear models -- Multiple linear regression models -- Partially linear multiple regression model -- Liu regression models -- Autoregressive models -- High-dimensional models -- Rank-based logistic regression -- Rank-based neural networks.
요약주기
"The purpose of this book is to lay the groundwork for robust data science using rankbased methods. The field of machine learning has not yet fully embraced a class of robust estimators that would address issues that limit the value of least-squares estimation. For example, outliers in data sets may produce misleading results that are not suitable for inference. They can also affect results obtained from penalty estimators. We believe that robust estimators for regression problems are well-suited to data science. This book is intended to provide both practical and mathematical foundations in the study of rank-based methods. It will introduce a number of new ideas and approaches to the practice and theory of robust estimation and encourage readers to pursue further investigation in this field. While the main goal of this book is to provide a rigorous treatment of the subject matter, we begin with some introductory material to build insight and intuition about rank-based regression and penalty estimators, especially for those who are new to the topic and those looking to understand key concepts. To motivate the need for such methods, we will start with a discussion of the median as it is the key to rank-based methods and then build on that concept towards the notion of robust data science"-- Provided by publisher.
ISBN
9781119625391
청구기호
519.536 S163r
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