Estimation in semiparametric models with missing data

This paper considers the problem of parameter estimation in a general class of semiparametric models when observations are subject to missingness at random. The semiparametric models allow for estimating functions that are non-smooth with respect to the parameter. We propose a nonparametric imputati...

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Bibliographic Details
Published inAnnals of the Institute of Statistical Mathematics Vol. 65; no. 4; pp. 785 - 805
Main Authors Chen, Song Xi, Van Keilegom, Ingrid
Format Journal Article
LanguageEnglish
Published Tokyo Springer Japan 01.08.2013
Springer Nature B.V
Subjects
Asymptotic properties
Computer simulation
Economics
Estimating
Estimators
Finance
Generalized linear models
Insurance
Management
Mathematical analysis
Mathematical models
Mathematics
Mathematics and Statistics
Missing data
Parameter estimation
Samples
Statistical analysis
Statistics
Statistics for Business
Studies
Copulas
Single-index model
Missing at random
Nuisance function
Imputation
Partially linear model
Kernel smoothing
Semiparametric model
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Summary:This paper considers the problem of parameter estimation in a general class of semiparametric models when observations are subject to missingness at random. The semiparametric models allow for estimating functions that are non-smooth with respect to the parameter. We propose a nonparametric imputation method for the missing values, which then leads to imputed estimating equations for the finite dimensional parameter of interest. The asymptotic normality of the parameter estimator is proved in a general setting, and is investigated in detail for a number of specific semiparametric models. Finally, we study the small sample performance of the proposed estimator via simulations.
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ISSN:0020-3157
1572-9052
DOI:10.1007/s10463-012-0393-6