NONPARAMETRIC PROBABILITY DENSITY ESTIMATION BY MAXIMUM PENALIZED LIKELIHOOD METHOD FOR NEGATIVELY DEPENDENT SEQUENCE

被引：0

作者：

Shirazi, E. ^{[1
]}

Nirumand, H. A. ^{[1
]}

Doosti, H. ^{[1
]}

机构：

[1] Ferdowsi Univ Mashhad, Dept Stat, Sch Math Sci, Mashhad, Iran

来源：

PAKISTAN JOURNAL OF STATISTICS | 2008年 / 24卷 / 02期

关键词：

Nonparametric density estimation; Maximum penalized likelihood; Wavelet; Negatively dependent;

D O I：

暂无

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

In this paper we introduce nonlinear regularized wavelet estimator for estimating nonparametric probability density when sample points are negatively dependent sequence. For this purpose we use penalized log likelihood method by penalizing the L(1) norm of the wavelet coefficients of the log density. Asymptotic rates of convergence of the estimator are also investigated and obtained an adapt estimator without prior knowledge of its regularity.

引用

页码：99 / 109

页数：11

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