Jackknife empirical likelihood of error variance in partially linear varying-coefficient errors-in-variables models

Ai-Ai Liu and Han-Ying Liang ()
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Ai-Ai Liu: Tongji University
Han-Ying Liang: Tongji University

Statistical Papers, 2017, vol. 58, issue 1, No 6, 95-122

Abstract: Abstract For the partially linear varying-coefficient model when the parametric covariates are measured with additive errors, the estimator of the error variance is defined based on residuals of the model. At the same time, we construct Jackknife estimator as well as Jackknife empirical likelihood statistic of the error variance. Under both the response variables and their associated covariates form a stationary $$\alpha $$ α -mixing sequence, we prove that the proposed estimators and Jackknife empirical likelihood statistic are asymptotic normality and asymptotic $$\chi ^2$$ χ 2 distribution, respectively. Numerical simulations are carried out to assess the performance of the proposed method.

Keywords: Asymptotic normality; Error variance; Jackknife empirical likelihood; Varying-coefficient errors-in-variables model; $$\alpha $$ α -Mixing; 62E20; 62J10 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s00362-015-0689-8

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