کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1146136 | 957497 | 2010 | 16 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Nonparametric kernel regression estimation for functional stationary ergodic data: Asymptotic properties Nonparametric kernel regression estimation for functional stationary ergodic data: Asymptotic properties](/preview/png/1146136.png)
The aim of this paper is to study asymptotic properties of the kernel regression estimate whenever functional stationary ergodic data are considered. More precisely, in the ergodic data setting, we consider the regression of a real random variable YY over an explanatory random variable XX taking values in some semi-metric abstract space. While estimating the regression function using the well-known Nadaraya–Watson estimator, we establish the consistency in probability, with a rate, as well as the asymptotic normality which induces a confidence interval for the regression function usable in practice since it does not depend on any unknown quantity. We also give the explicit form of the conditional bias term. Note that the ergodic framework is more convenient in practice since it does not need the verification of any condition as in the mixing case for example.
Journal: Journal of Multivariate Analysis - Volume 101, Issue 10, November 2010, Pages 2266–2281