Subset selection in regression (Titelsatznr. 53400)

[ MARC ]
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
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e ocip
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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
Exemplare
Withdrawn status Lost status Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Cost, normal purchase price Full call number Barcode Date last seen Copy number Price effective from Koha item type
        6october 6october 1104 2006-12-26 0.00 519.536 M S SOULE104001345 2021-11-22 1 2015-07-12 Books
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