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LEADER: 03468cam a2200685Ia 4500
001 15068449
005 20220604231641.0
006 m o d
007 cr cnu---unuuu
008 060822s2002 flua ob 001 0 eng d
010 $a 2002020214
035 $a(OCoLC)ocm71014453
035 $a(NNC)15068449
040 $aN$T$beng$epn$cN$T$dYDXCP$dOCLCQ$dIDEBK$dCAI$dE7B$dOCLCQ$dOHS$dOCLCQ$dMERUC$dTULIB$dOCLCO$dOCLCQ$dOCLCA$dOCLCF$dCRCPR$dOCLCQ$dUAB$dERL$dOCLCO$dOCLCQ$dNLE$dOCLCO$dUKMGB$dOCLCO$dWYU$dOCLCA$dYDX$dLEAUB$dUKAHL$dOL$$dOCLCO
015 $aGBB7C6211$2bnb
016 7 $a018426663$2Uk
019 $a150388294$a276796497$a647585732$a779914062$a1031048551$a1066012035$a1102528865
020 $a1420035932$q(electronic bk.)
020 $a9781420035933$q(electronic bk.)
020 $a9781584881711
020 $a1584881712
020 $z1584881712$q(cloth)
035 $a(OCoLC)71014453$z(OCoLC)150388294$z(OCoLC)276796497$z(OCoLC)647585732$z(OCoLC)779914062$z(OCoLC)1031048551$z(OCoLC)1066012035$z(OCoLC)1102528865
037 $aTANDF_184049$bIngram Content Group
050 4 $aQA278.2$b.M56 2002eb
060 4 $aQA 278.2
072 7 $aMAT$x029030$2bisacsh
082 04 $a519.5/36$222
049 $aZCUA
100 1 $aMiller, Alan J.
245 10 $aSubset selection in regression /$cAlan Miller.
250 $a2nd ed.
260 $aBoca Raton :$bChapman & Hall/CRC,$c©2002.
300 $a1 online resource (xvii, 238 pages) :$billustrations.
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
490 1 $aMonographs on statistics and applied probability ;$v95
504 $aIncludes bibliographical references (pages 223-234) and index.
588 0 $aPrint version record.
505 0 $aPreface to first edition; Preface to second edition; 1 Objectives; 2 Least-squares computations; 3 Finding subsets which fit well; 4 Hypothesis testing; 5 When to stop?; 6 Estimation of regression coefficients; 7 Bayesian subset selection; 8 Conclusions and somer ecommendations; References; Index.
520 $aMiller (Commonwealth Scientific & Industrial Research Organization, Australia), focusing almost entirely on nonlinear regression, presents a monograph that collects what is known about estimation techniques 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 d.
650 0 $aRegression analysis.
650 0 $aLeast squares.
650 2 $aRegression Analysis
650 2 $aLeast-Squares Analysis
650 6 $aAnalyse de régression.
650 6 $aMoindres carrés.
650 7 $aMATHEMATICS$xProbability & Statistics$xRegression Analysis.$2bisacsh
650 7 $aLeast squares.$2fast$0(OCoLC)fst00995082
650 7 $aRegression analysis.$2fast$0(OCoLC)fst01432090
650 17 $aRegressieanalyse.$2gtt
650 17 $aLineaire regressie.$2gtt
650 17 $aKleinste-kwadratenmethode.$2gtt
655 0 $aElectronic books.
655 4 $aElectronic books.
776 08 $iPrint version:$aMiller, Alan J.$tSubset selection in regression.$b2nd ed.$dBoca Raton : Chapman & Hall/CRC, ©2002$z1584881712$w(DLC) 2002020214$w(OCoLC)48951350
830 0 $aMonographs on statistics and applied probability (Series) ;$v95.
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15068449$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS