The empirical likelihood-based
inference for varying coefficient models with missing covariates is
investigated. An imputed empirical likelihood ratio function for the
coefficient functions is proposed, and it is shown that iis limiting
distribution is standard chi-squared. Then the corresponding confidence
intervals for the regression coefficients are constructed. Some simulations
show that the proposed procedure can attenuate the effect of the missing data,
and performs well for the finite sample.
Cite this paper
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