Please use this identifier to cite or link to this item: http://archives.univ-biskra.dz/handle/123456789/2259
Title: Asymptotic Normality of a Kernel Conditional Quantile Estimator Under Strong Mixing Hypothesis and Left-Truncation
Authors: Elias Ould Saïd
Djabrane Yahia
Keywords: Asymptotic normality
Conditional quantile
Kernel estimate
Strong mixing
Truncated data
Issue Date: 11-Apr-2013
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
URI: http://archives.univ-biskra.dz/handle/123456789/2259
Appears in Collections:Publications Internationales



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