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• W2059575129 abstract "In this paper, we investigate the empirical likelihood inferences of varying coefficient errors-in-variables models with longitudinal data. The naive empirical log-likelihood ratios for the time-varying coefficient function based on the global and local variance structures are introduced. The corresponding maximum empirical likelihood estimators of the time-varying coefficients are derived, and their asymptotic properties are established. Wilks’ phenomenon of the naive empirical log-likelihood ratio, which ignores the within subject correlation, is proven through the employment of undersmoothing. To avoid the undersmoothing, we recommend a residual-adjust empirical log-likelihood ratio and prove that its asymptotic distribution is standard chi-squared. Thus, this result can be used to construct the confidence regions of the time-varying coefficients. We also establish the asymptotic distribution theory for the corresponding residual-adjust maximum empirical likelihood estimator and find it to be unbiased even when an optimal bandwidth is used. Furthermore, we consider the construction of the pointwise confidence interval for a component of the time-varying coefficients and provide the simulation studies to assess the finite sample performance, while we conduct a real example to illustrate the proposed method." @default.
• W2059575129 created "2016-06-24" @default.
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• W2059575129 date "2014-05-01" @default.
• W2059575129 modified "2023-10-18" @default.
• W2059575129 title "Empirical likelihood of varying coefficient errors-in-variables models with longitudinal data" @default.
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• W2059575129 doi "https://doi.org/10.1016/j.jmva.2014.02.004" @default.
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