scholarly journals Strong uniform consistency of a kernel conditional quantile estimator for censored and associated data

2018 ◽  
Vol 48 (1) ◽  
Author(s):  
Wafaa Djelladj ◽  
Abdelkader Tatachak
Keyword(s):  
Uniform Consistency ◽  
Conditional Quantile ◽  
Strong Uniform Consistency ◽  
Conditional Quantile Estimator ◽  
Associated Data

scholarly journals Asymptotic Normality of a Nonparametric Conditional Quantile Estimator for Random Fields

2011 ◽  
Vol 2011 ◽  
pp. 1-35
Author(s):  
Sidi Ali Ould Abdi ◽  
Sophie Dabo-Niang ◽  
Aliou Diop ◽  
Ahmedoune Ould Abdi
Keyword(s):  
Asymptotic Normality ◽  
Random Fields ◽  
Quantile Function ◽  
Spatial Process ◽  
Kernel Estimate ◽  
Conditional Quantile ◽  
Conditional Quantile Estimator ◽  
Conditional Quantile Function ◽  
Mixing Sequence ◽  
Explicative Variable

Given a stationary multidimensional spatial process , we investigate a kernel estimate of the spatial conditional quantile function of the response variable given the explicative variable . Asymptotic normality of the kernel estimate is obtained when the sample considered is an -mixing sequence.

scholarly journals Strong uniform consistency rates of conditional quantile estimation in the single functional index model under random censorship

2018 ◽  
Vol 6 (1) ◽  
pp. 197-227 ◽  
Author(s):  
Nadia Kadiri ◽  
Abbes Rabhi ◽  
Amina Angelika Bouchentouf
Keyword(s):  
Cumulative Distribution Function ◽  
Complete Convergence ◽  
Asymptotic Properties ◽  
Cumulative Distribution ◽  
Index Structure ◽  
Functional Index ◽  
Conditional Quantile ◽  
Index Model ◽  
Strong Uniform Consistency ◽  
Mixing Sequence

AbstractThe main objective of this paper is to non-parametrically estimate the quantiles of a conditional distribution in the censorship model when the sample is considered as an -mixing sequence. First of all, a kernel type estimator for the conditional cumulative distribution function (cond-cdf) is introduced. Afterwards, we estimate the quantiles by inverting this estimated cond-cdf and state the asymptotic properties when the observations are linked with a single-index structure. The pointwise almost complete convergence and the uniform almost complete convergence (with rate) of the kernel estimate of this model are established. This approach can be applied in time series analysis.

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