MARC Record from marc_columbia

LEADER: 03075fam a22003614a 4500
001 3183793
005 20221020003452.0
008 010227t20022002caua b 001 0 eng
010 $a 2001001295
020 $a0761916725 (pbk. : acid-free paper)
035 $a(OCoLC)46364640
035 $a(OCoLC)ocm46364640
035 $9AUC7386CU
035 $a(NNC)3183793
035 $a3183793
040 $aDLC$cDLC$dDLC$dOrLoB-B
042 $apcc
050 00 $aQA276$b.A55 2002
100 1 $aAllison, Paul David.$0http://id.loc.gov/authorities/names/n80019142
245 10 $aMissing data /$cPaul D. Allison.
260 $aThousand Oaks, Calif. :$bSage Publications,$c[2002], ©2002.
300 $avi, 93 pages :$billustrations ;$c22 cm.
336 $atext$btxt$2rdacontent
337 $aunmediated$bn$2rdamedia
490 1 $aSage university papers. Quantitative applications in the social sciences ;$vno. 07-136
500 $a"A SAGE university paper"--Cover.
504 $aIncludes bibliographical references (p. 89-91) and index.
505 00 $g1.$tIntroduction --$g2.$tAssumptions.$tMissing Completely at Random.$tMissing at Random.$tIgnorable.$tNonignorable --$g3.$tConventionla Methods.$tListwise Deletion.$tPairwise Deletion.$tDummy Variable Adjustment.$tImputation.$tSummary --$g4.$tMaximum Likelihood.$tReview of Maximum Likelihood.$tML With Missing Data.$tContingency Table Data.$tLinear Models With Normally Distributed Data.$tThe EM Algorithm.$tEM Example.$tDirect ML.$tDirect ML Example.$tConclusion --$g5.$tMultiple Imputation: Basics.$tSingle Random Imputation.$tMultiple Random Imputation.$tAllowing for Random Variation in the Parameter Estimates.$tMultiple Imputation Under the Multivariate Normal Model.$tData Augmentation for the Multivariate Normal Model.$tConvergence in Data Augmentation.$tSequential Versus Parallel Chains of Data Augmentation.$tUsing the Normal Model for Nonnormal or Categorical Data.$tExploratory Analysis.$tMI Example 1 --$g6.$tMultiple Imputation: Complications.$tInteractions and Nonlinearities in MI.
505 80 $tCompatibility of the Imputation Model and the Analysis Model.$tRole of the Dependent Variable in Imputation.$tUsing Additional Variables in the Imputation Process.$tOther Parametric Approaches to Multiple Imputation.$tNonparametric and Partially Parametric Methods.$tSequential Generalized Regression Models.$tLinear Hypothesis Tests and Likelihood Ratio Tests.$tMI Example 2.$tMI for Longitudinal and Other Clustered Data.$tMI Example 3 --$g7.$tNonignorable Missing Data.$tTwo Classes of Models.$tHeckman's Model for Sample Selection Bias.$tML Estimation With Pattern-Mixture Models.$tMultiple Imputation With Pattern-Mixture Models --$g8.$tSummary and Conclusion.
650 0 $aMathematical statistics.$0http://id.loc.gov/authorities/subjects/sh85082133
650 0 $aMissing observations (Statistics)$0http://id.loc.gov/authorities/subjects/sh85086013
830 0 $aQuantitative applications in the social sciences ;$vno. 07-136.$0http://id.loc.gov/authorities/names/n42021487
852 00 $bleh$hQA276$i.A55 2002