Abstract
In this paper, we consider the empirical likelihood-based inferences for varying coefficient models Y = X τ α(U) + ɛ when X are subject to missing at random. Based on the inverse probability-weighted idea, a class of empirical log-likelihood ratios, as well as two maximum empirical likelihood estimators, are developed for α(u). The resulting statistics are shown to have standard chi-squared or normal distributions asymptotically. Simulation studies are also constructed to illustrate the finite sample properties of the proposed statistics.
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This work was supported in part by NSF of China (No.11461029), NSF of Jiangxi Province (No.20142BAB211014), and YSFP of Jiangxi provincial education department (No. GJJ14350).
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Liu, Xh. Empirical likelihood-based inferences in varying coefficient models with missing data. Acta Math. Appl. Sin. Engl. Ser. 31, 823–840 (2015). https://doi.org/10.1007/s10255-015-0508-y
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DOI: https://doi.org/10.1007/s10255-015-0508-y
Keywords
- varying coefficient models
- missing at random
- empirical likelihood
- maximum empirical likelihood estimator