Descripción del título
Missing data / Paul D. Allison
"Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis. Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has been relying on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data."--Pub. desc
Monografía
monografia Rebiun17185381 https://catalogo.rebiun.org/rebiun/record/Rebiun17185381 010227s2001 caua b 001 0 eng GBA164777 bnb 48363634 0761916725 pbk. : acid-free paper) 9780761916727 pbk. : acid-free paper) AU@ 000022443165 AU@ 000022976031 AU@ 000023171250 DEBBG BV017326025 HEBIS 100136591 IG# 0761916725 IG# 9780761916727 LQU 192645 NZ1 6115228 YDXCP 100274775 UM0375653 UCLM0706443 CIS0020735 UIB 47939446 UOV1080158 UAM 991000709189704211 UPCT u56584 UCAR 991000578019704213 UPNA0138731 IEF 76107 DLC. eng. DLC. UKM. LVB. UGX. BAKER. NLGGC. WAU. IG#. BTCTA. YDXCP. DEBBG. BDX. MOF. LHU. OCLCF. BEDGE. OCLCQ pcc 001.4/22 21 519.54 21 70.03 bcl MR 2100 rvk MR 2200 rvk QH 235 rvk Allison, Paul David Missing data Paul D. Allison Thousand Oaks, Calif. Sage Publications 2002 Thousand Oaks, Calif. Thousand Oaks, Calif. Sage Publications vi, 93 pages illustrations 22 cm vi, 93 pages Sage university papers. Quantitative applications in the social sciences no. 07-136 "A SAGE university paper"--Cover Includes bibliographical references (pages 89-91) and index 1. Introduction -- 2. Assumptions ; Missing Completely at Random ; Missing at Random ; Ignorable ; Nonignorable -- 3. Conventional Methods ; Listwise ; Deletion; Pairwise Deletion ; Dummy Variable Adjustment ; Imputation -- 4. Maximum Likelihood ; Review of Maximum Likelihood ; ML With Missing Data ; Contingency Table Data ; Linear Models With Normally Distributed Data ; The EM Algorithm ; EM Example ; Direct ML ; Direct ML Example -- 5. Multiple Imputation: Bascis ; Single Random Imputation ; Multiple Random Imputation ; Allowing for Random Variation in the Parameter Estimates ; Multiple Imputation Under the Multivariate Normal Model ; Data Augmentation for the Multivariate Normal Model ; Convergence in Data Augmentation ; Sequential Verses Parallel Chains of Data Augmentation ; Using the Normal Model for Nonnormal or Categorical Data ; Exploratory Analysis -- 6. Multiple Imputation: Complications ; Interactions and Nonlinearities in MI ; Compatibility of the Imputation Model and the Analysis Model ; Role of the Dependent Variable in Imputation ; Using Additional Variables in the Imputation Process ; Other Parametric Approaches to Multiple Imputation ; Nonparametric and Partially Parametric Methods ; Sequential Generalized Regression Models ; Linear Hypothesis Tests and Likelihood Ratio Tests -- 7. Nonignorable Missing Data ; Two Classes of Models ; Heckman's Model for Sample Selection Bias ; ML Estimation With Pattern-Mixture Models ; Multiple Imputation With Pattern-Mixture Models "Sooner or later anyone who does statistical analysis runs into problems with missing data in which information for some variables is missing for some cases. Why is this a problem? Because most statistical methods presume that every case has information on all the variables to be included in the analysis. Using numerous examples and practical tips, this book offers a nontechnical explanation of the standard methods for missing data (such as listwise or casewise deletion) as well as two newer (and, better) methods, maximum likelihood and multiple imputation. Anyone who has been relying on ad-hoc methods that are statistically inefficient or biased will find this book a welcome and accessible solution to their problems with handling missing data."--Pub. desc Mathematical statistics Missing observations (Statistics) Estadística matemática Statistique mathématique Observations manquantes (Statistique) Analyse des données. eclas Données statistiques. eclas Modèles économiques. eclas Méthodes statistiques. eclas Sciences sociales. eclas Mathematical statistics. fast Missing observations (Statistics). fast Ontbrekende gegevens. gtt Statistiek. gtt Fehlende Daten. gnd Statistik. gnd Fehlende Daten. swd Statistik. swd Quantitative applications in the social sciences no. 07-136