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Statistical analysis with missing data Roderick J. A. Little ; Donald B. Rubin

By: Little, Roderick JContributor(s): Rubin, Donald BMaterial type: TextTextSeries: Wiley series in probability and statisticsPublication details: Hoboken, NJ Wiley 2002 Edition: 2nd edDescription: xv, 381 S. graph. DarstISBN: 0471183865Subject(s): statistische MethodeDDC classification: 519.5 LOC classification: QA276Other classification: C.1. Summary: PART I: OVERVIEW AND BASIC APPROACHES. - Introduction. - Missing Data in Experiments. - Complete-Case and Available-Case Analysis, Including Weighting Methods. . - Single Imputation Methods. - Estimation of Imputation Uncertainty. - PART II: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA. - Theory of Inference Based on the Likelihood Function. . - Methods Based on Factoring the Likelihood, Ignoring the Missing-Data Mechanism. - Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse. - Large-Sample Inference Based on Maximum Likelihood Estimates. - Bayes and Multiple Imputation. - PART III: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA: APPLICATIONS TO SOME COMMON MODELS. - Multivariate Normal Examples, Ignoring the Missing-Data Mechanism. - Models for Robust Estimation. - Models for Partially Classified Contingency Tables, Ignoring the Missing-Data Mechanism. - Mixed Normal and Nonnormal Data with Missing Values, Ignoring the Missing-Data Mechanism. - Nonignorable Missing-Data Models.
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Holdings
Item type Current library Call number Status Date due Barcode Item holds
Monographie ausleihbar Monographie ausleihbar Gemeinsame Bibliothek
Lesesaal
19/M 06.0246 (Browse shelf(Opens below)) Available 000444649
Total holds: 0

MAB0014.001: M 06.0246

PART I: OVERVIEW AND BASIC APPROACHES. - Introduction. - Missing Data in Experiments. - Complete-Case and Available-Case Analysis, Including Weighting Methods. . - Single Imputation Methods. - Estimation of Imputation Uncertainty. - PART II: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA. - Theory of Inference Based on the Likelihood Function. . - Methods Based on Factoring the Likelihood, Ignoring the Missing-Data Mechanism. - Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse. - Large-Sample Inference Based on Maximum Likelihood Estimates. - Bayes and Multiple Imputation. - PART III: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA: APPLICATIONS TO SOME COMMON MODELS. - Multivariate Normal Examples, Ignoring the Missing-Data Mechanism. - Models for Robust Estimation. - Models for Partially Classified Contingency Tables, Ignoring the Missing-Data Mechanism. - Mixed Normal and Nonnormal Data with Missing Values, Ignoring the Missing-Data Mechanism. - Nonignorable Missing-Data Models.

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