Mendeley出力
＜図書＞
Subset selection in regression
| 責任表示 | Alan Miller |
|---|---|
| シリーズ | Monographs on statistics and applied probability ; 95 |
| データ種別 | 図書 |
| 版 | 2nd ed |
| 出版情報 | Boca Raton : Chapman & Hall/CRC , c2002 |
| 本文言語 | 英語 |
| 大きさ | xvii, 238 p. : ill. ; 24 cm |
| 概要 | Miller (Commonwealth Scientific & Industrial Research Organization, Australia), focusing almost entirely on nonlinear regression, presents a monograph that collects what is known about estimation tech...iques and discusses some new material. It is the aim of the author to provide information on the proper empirical choice of a model, given a set of data and many variables or alternative models from which to select. This new edition adds material on Bayesiean methods, as well as stochastic algorithms for finding good subsets from large numbers of predictors. Annotation copyrighted by Book News, Inc., Portland, OR.続きを見る |
| 目次 | Machine generated contents note: 1 Objectives 1.1 Prediction, explanation, elimination or what? 1.2 How many variables in the prediction formula? 1.3 Alternatives to using subsets 1.4 'Black box' use of best-subsets techniques 2 Least-squares computations 2.1 Using sums of squares and products matrices 2.2 Orthogonal reduction methods 2.3 Gauss-Jordan v. orthogonal reduction methods 2.4 Interpretation of projections Appendix A. Operation counts for all-subsets regression A.1 Garside's Gauss-Jordan algorithm A.2 Planar rotations and a Hamiltonian cycle A.3 Planar rotations and a binary sequence A.4 Fast planar rotations 3 Finding subsets which fit well 3.1 Objectives and limitations of this chapter 3.2 Forward selection 3.3 Efroymson's algorithm 3.4 Backward elimination 3.5 Sequential replacement algorithms 3.6 Replacing two variables at a time 3.7 Genierating all subsets 3.8 Using branch-and-bound techniques 3.9 Grouping variables 3.10 Ridge regression and other alternatives 3.11 The nonnegative garrote and the lasso 3.12 Some examples 3.13 Conclusions and recommendations Appendix A. An algorithm for the lasso 4 Hypothesis testing 4.1 Is there any information in the remaining variables? 4.2 Is one subset better than another? 4.2.1 Applications of Spj-tvoll's method 4.2.2 Using other confidence ellipsoids Appendix A.Spjotvoll's method - detailed description 5 When to stop? 5.1 What criterion should we use? 5.2 Prediction criteria 5.2.1 Mean squared errors of prediction (MSEP) 5.2.2 MSEP for the fixed model 5.2.3 MSEP for the random model 5.2.4 A simulation with random predictors 5.3 Cross-validation and the P SS statistic 5.4 Bootstrapping 5.5 Likelihood and information-based stopping rules 5.5.1 Minimum description length (MDL) Appendix A. Approximate equivaence of stppingules A.1 F-to-enter A.2 Adjusted R2 or Fisher's A-statistic A.3 Akaikesinformatibn criterion (AIC) 6 Estatmaion of regression eficients 6.1 Selection bias 6.2 Choice between two varies 6.3 Selection rduction 6.3.1 Monte C o et tionfias i f d lection 6.3.2 Shrinkage methods 6.3.3 Using the jack-knife 6.3.4 Independent; data sets ; 6.4 Conditional likiood estimations 6.5 Estimationofpopulation means 6.6 Estimating least-squares projections ; Appendix A. Changing projections to equate sums of squares 7 Bayesian mnethods 7.1 Bayesian introduction 7.2 'Spike and slab'prior 7.3 Normal prior for regression coefficients 7.4 Model averaging 7.5 Picking the best model 8 Conclusions and some recommendations References Index.続きを見る |
所蔵情報
| 状態 | 巻次 | 所蔵場所 | 請求記号 | 刷年 | 文庫名称 | 資料番号 | コメント | 予約・取寄 | 複写申込 | 自動書庫 |
|---|---|---|---|---|---|---|---|---|---|---|
|
|
|
理系図3F 数理独自 | MILL/20/1a | 2002 |
|
023212002003743 |
|
書誌詳細
| 一般注記 | Bibliography: p. 223-234 Includes index |
|---|---|
| 著者標目 | *Miller, Alan J., 1936- |
| 件 名 | LCSH:Regression analysis LCSH:Least squares |
| 分 類 | LCC:QA278.2 DC20:519.5/36 |
| 書誌ID | 1001400466 |
| ISBN | 1584881712 |
| NCID | BA56801314 |
| 巻冊次 | ISBN:1584881712 |
| 登録日 | 2009.11.02 |
| 更新日 | 2009.11.02 |