| 000 -LEADER |
|---|
| fixed length control field |
04160cam a2200361 a 4500 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20150712005511.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
020125s2002 flua b 001 0 eng |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2002020214 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
1584881712 (acid-free paper) |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
DLC |
| Transcribing agency |
DLC |
| Modifying agency |
DLC |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA278.2 |
| Item number |
.M56 2002 |
| 082 00 - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
519.536 |
| Edition number |
21 |
| 084 ## - OTHER CLASSIFICATION NUMBER |
|---|
| Classification number |
519.536 |
| Item number |
M S |
| 001 - CONTROL NUMBER |
|---|
| control field |
0000046416 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
0000 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Miller, Alan J. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Subset selection in regression |
| Medium |
[[Book] /] |
| Statement of responsibility, etc. |
Alan Miller. |
| 250 ## - EDITION STATEMENT |
|---|
| Edition statement |
2nd ed. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc. |
Boca Raton : |
| Name of publisher, distributor, etc. |
Chapman & Hall/CRC, |
| Date of publication, distribution, etc. |
c2002. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvii, 238 p. : |
| Other physical details |
ill. ; |
| Dimensions |
24 cm. |
| 440 #0 - SERIES STATEMENT/ADDED ENTRY--TITLE |
|---|
| Title |
Monographs on statistics and applied probability ; |
| Volume/sequential designation |
95 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references (p. 223-234) and index. |
| 505 8# - FORMATTED CONTENTS NOTE |
|---|
| Formatted contents note |
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. |
| 521 ## - TARGET AUDIENCE NOTE |
|---|
| Target audience note |
All Ages. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Regression analysis. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Least squares. |
| 856 41 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
Table of contents only |
| Uniform Resource Identifier |
http://www.loc.gov/catdir/toc/fy022/2002020214.html |
| 856 42 - ELECTRONIC LOCATION AND ACCESS |
|---|
| Materials specified |
Publisher description |
| Uniform Resource Identifier |
http://www.loc.gov/catdir/enhancements/fy0646/2002020214-d.html |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| a |
7 |
| b |
cbc |
| c |
orignew |
| d |
1 |
| e |
ocip |
| f |
20 |
| g |
y-gencatlg |
| 925 0# - |
|---|
| -- |
acquire |
| -- |
2 shelf copies |
| -- |
policy default |
| 955 ## - COPY-LEVEL INFORMATION (RLIN) |
|---|
| -- |
jp99 2002-02-01 |
| -- |
jp85 2002-02-04 to Dewey |
| -- |
pv12 2002-06-24 CIP ver. to BCCD |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Koha item type |
Books |
