BOOK-CHAPTER

Missing-data imputation

Andrew GelmanJennifer Hill

Year: 2006 Cambridge University Press eBooks Pages: 529-544   Publisher: Cambridge University Press

Abstract

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

Keywords:
Missing data Computer science Imputation (statistics) Multilevel model Data mining Inference Causal inference Regression analysis Logistic regression Regression Statistics Machine learning Artificial intelligence Mathematics

Metrics

101
Cited By
1.85
FWCI (Field Weighted Citation Impact)
0
Refs
0.86
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Survey Sampling and Estimation Techniques
Physical Sciences →  Mathematics →  Statistics and Probability

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