empirical likelihood for multidimensional linear model with missing responses
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ID: 150672
2012
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Abstract
Imputation is a popular technique for handling missing data especially for plenty
of missing values. Usually, the empirical log-likelihood ratio statistic under imputation
is asymptotically scaled chi-squared because the imputing data are not i.i.d.
Recently, a bias-corrected technique is used to study linear regression model with
missing response data, and the resulting empirical likelihood ratio is asymptotically
chi-squared. However, it may suffer from the “the curse of high dimension” in multidimensional
linear regression models for the nonparametric estimator of selection
probability function. In this paper, a parametric selection probability function is
introduced to avoid the dimension problem. With the similar bias-corrected method,
the proposed empirical likelihood statistic is asymptotically chi-squared when the selection
probability is specified correctly and even asymptotically scaled chi-squared
when specified incorrectly. In addition, our empirical likelihood estimator is always
consistent whether the selection probability is specified correctly or not, and will
achieve full efficiency when specified correctly. A simulation study indicates that
the proposed method is comparable in terms of coverage probabilities.
| Reference Key |
zhu2012journalempirical
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|---|---|
| Authors | ;Liping Zhu |
| Journal | nature protocols |
| Year | 2012 |
| DOI |
10.1155/2012/473932
|
| URL | |
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