Asymptotic Normality of a Kernel Conditional Quantile Estimator Under Strong Mixing Hypothesis and Left-Truncation

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DC Field	Value	Language
dc.contributor.author	Elias Ould Saïd	-
dc.contributor.author	Djabrane Yahia	-
dc.date.accessioned	2013-04-11T12:26:51Z	-
dc.date.available	2013-04-11T12:26:51Z	-
dc.date.issued	2013-04-11	-
dc.identifier.uri	http://archives.univ-biskra.dz/handle/123456789/2259	-
dc.description.abstract	We consider the estimation of the conditional quantile when the interest variable is subject to left truncation. Under regularity conditions, it is shown that the kernel estimate of the conditional quantile is asymptotically normally distributed, when the data exhibit some kind of dependence. We use asymptotic normality to construct confidence bands for predictors based on the kernel estimate of the conditional median.DOI:10.1080/03610926.2010.489171 Link http://www.tandfonline.com/doi/abs/10.1080/03610926.2010.489171	en_US
dc.subject	Asymptotic normality	en_US
dc.subject	Conditional quantile	en_US
dc.subject	Kernel estimate	en_US
dc.subject	Strong mixing	en_US
dc.subject	Truncated data	en_US
dc.title	Asymptotic Normality of a Kernel Conditional Quantile Estimator Under Strong Mixing Hypothesis and Left-Truncation	en_US
dc.type	Article	en_US
Appears in Collections:	Publications Internationales

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