Deconvolving multivariate kernel density estimates from contaminated associated observations

被引：9
|
作者：
Masry, E [1 ]
机构：
[1] Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA
来源：
IEEE TRANSACTIONS ON INFORMATION THEORY | 2003年 / 49卷 / 11期
关键词：
associated processes; asymptotic normality; deconvolution of multivariate probability densities; quadratic-mean convergence;
D O I：
10.1109/TIT.2003.818415
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We consider the estimation of the multivariate probability density function f (x(1),...,x(p)) of X-1,...,X-p of a stationary positively or negatively associated ((PA or (NA)) random process {X-i}(i=1)(infinity) from noisy observations. Both ordinary smooth and super smooth noise are considered. Quadratic mean and asymptotic normality results are established.
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页码：2941 / 2952
页数：12
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