A hybrid approach for regression analysis with block missing data

Missing data often arise in practice. The commonly employed approach to handle the missing data is imputation, which is effective when the missing mechanism is known and each subject in the data set misses at random. However, the situation where the imputation is not appropriate often emerged. Becau...

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Bibliographic Details
Published inComputational statistics & data analysis Vol. 75; pp. 239 - 247
Main Authors Li, Zhengbang, Li, Qizhai, Han, Chien-Pai, Li, Bo
Format Journal Article
LanguageEnglish
Published Elsevier B.V 01.07.2014
Subjects
Bayesian
Bayesian theory
Block missing
Blocking
Computer simulation
data collection
Data processing
Estimates
Estimators
Frequentist
Hybrid
Likelihood
Mathematical models
Missing data
regression analysis
Statistics
Hybrid
Block missing
Frequentist
Likelihood
Bayesian
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Summary:Missing data often arise in practice. The commonly employed approach to handle the missing data is imputation, which is effective when the missing mechanism is known and each subject in the data set misses at random. However, the situation where the imputation is not appropriate often emerged. Because in that situation, some data are not missing at random, so a hybrid estimate, where the Bayesian and frequentist approaches are used for inferring the parameters with and without prior information respectively, is proposed. The asymptotic properties of the hybrid estimator are also provided. Numerical results including simulation studies and data analysis about grade point average (GPA) are conducted to show the performances of the proposed method.
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ISSN:0167-9473
1872-7352
DOI:10.1016/j.csda.2014.02.014