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Asymptotic Normality of Wavelet Density Estimator under Censored Dependent Observations

Asymptotic Normality of Wavelet Density Estimator under Censored Dependent Observations
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摘要 In this paper, we discuss the asymptotic normality of the wavelet estimator of the density function based on censored data, when the survival and the censoring times form a stationary α-mixing sequence. To simulate the distribution of estimator such that it is easy to perform statistical inference for the density function, a random weighted estimator of the density function is also constructed and investigated. Finite sample behavior of the estimator is investigated via simulations too. In this paper, we discuss the asymptotic normality of the wavelet estimator of the density function based on censored data, when the survival and the censoring times form a stationary α-mixing sequence. To simulate the distribution of estimator such that it is easy to perform statistical inference for the density function, a random weighted estimator of the density function is also constructed and investigated. Finite sample behavior of the estimator is investigated via simulations too.
作者 Si-li NIU
出处 《Acta Mathematicae Applicatae Sinica》 SCIE CSCD 2012年第4期781-794,共14页 应用数学学报(英文版)
基金 Supported by the National Natural Science Foundation of China (No.10871146)
关键词 Wavelet density estimator asymptotic normality censored data α-mixing random weightedestimator Wavelet density estimator asymptotic normality, censored data, α-mixing, random weightedestimator
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