The kernel regression estimation for randomly censored functional stationary ergodic data

Nengxiang Ling and Yang Liu

Communications in Statistics - Theory and Methods, 2017, vol. 46, issue 17, 8557-8574

Abstract: In this paper, we investigate the asymptotic properties of the kernel estimator for non parametric regression operator when the functional stationary ergodic data with randomly censorship are considered. More precisely, we introduce the kernel-type estimator of the non parametric regression operator with the responses randomly censored and obtain the almost surely convergence with rate as well as the asymptotic normality of the estimator. As an application, the asymptotic (1 − ζ) confidence interval of the regression operator is also presented (0

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:46:y:2017:i:17:p:8557-8574

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DOI: 10.1080/03610926.2016.1185117

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