Asymptotic Normality of Kernel Type Density Estimators for Random Fields

2006 ◽  
Vol 9 (2) ◽  
pp. 161-178 ◽  
Author(s):  
István Fazekas ◽  
Alexey Chuprunov
Keyword(s):  
Asymptotic Normality ◽  
Random Fields ◽  
Kernel Type ◽  
Density Estimators

scholarly journals A Functional Central Limit Theorem for Kernel Type Density Estimators

2016 ◽  
Vol 35 (4) ◽  
Author(s):  
István Fazekas ◽  
Peter Filzmoser
Keyword(s):  
Central Limit Theorem ◽  
Limit Theorem ◽  
Central Limit ◽  
Random Fields ◽  
Functional Central Limit Theorem ◽  
Integrable Functions ◽  
Square Integrable Functions ◽  
Functional Central Limit ◽  
Kernel Type ◽  
Density Estimators

Kernel type density estimators are studied for random fields. A functional central limit theorem in the space of square integrable functions is proved if the locations of observations become more and more dense in an increasing sequence of domains.

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.

Asymptotic normality for wavelet deconvolution density estimators

2020 ◽  
Vol 48 (1) ◽  
pp. 321-342 ◽  
Author(s):  
Youming Liu ◽  
Xiaochen Zeng
Keyword(s):  
Asymptotic Normality ◽  
Density Estimators
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