左截断右删失数据下半参数模型风险率函数估计

文章给出了右删失左截断数据半参数模型下的风险率函数估计,讨论了风险率函数估计的渐近性质,获得了这些估计的渐近正态性,对数律和重对数律.由于假定删失机制服从半参数模型下,从而知道模型的更多信息,因此对于给出参数的极大似然估计,可以改进风险率函数估计的渐近性质.也就是说,删失数据模型具有半参数的辅助信息下, 风险率函数估计的渐近方差比通常的完全非参数的估计的渐近方差更小.这说明加入了额外的信息提高了风险率函数估计的效率.     

左删失右截断数据的分位数的固定宽度序贯置信区间估计

基于左截断右删失数据下的乘积限估计构造了分位数固定宽度序贯置信区间及其估计，研究了序贯置信区间估计的渐近性质。作为副产品，获得了分位数估计近邻点的Bahadur表示定理。这个表示定理是推导分位数固定宽度序贯置信区间估计渐近性质的重要基础。同时，在文中，进行了一些计算机模拟试验，证明了左截断右删失数据下分位数估计的序贯方法是效的和精确的。     

A NOTE ON OSCILLATION MODULUS OF PL-PROCESS AND ITS APPLICATIONS UNDER RANDOM CENSORSHIP

The strong limit results of oscillation modulus of PL-process are established in this paper when the density function is not continuous function for censored data. The rates of convergence of oscillation modulus of PL-process are sharp under week condition. These results can be used to derive laws of the iterated logarithm of random bandwidth kernel estimator and nearest neighborhood estimator of density under continuous conditions of density function being not assumed.     

非参数回归函数的kn近邻估计的渐近性质及其Bootstrap逼近

本文研究回归函数的kn-近邻估计的渐近性质,得到了回归函数的kn－近邻估计的渐近正态性和它的Bootstrap统计量的相合性,在高阶矩存在的条件下,我们证明了回归函数的kn-近邻估计的Bootstrap逼近比正态逼近更精确。     

STRONG REPRESENTATIONS OF THE SURVIVAL FUNCTION ESTIMATOR ON INCREASING SETS FOR TRUNCATED AND CENSORED DATA

1991MRSubjectClassification62G05,62E201IntroductionandtheMainResultsInthisarticlewestudysllrvivaldatawhicharesubjecttobothlefttrull(:ationandrightcensoring(LTRC).Morespecifically,let(X,T,Y)denoterandomvariableswhereXisthevariableofinterest,calledthelifetimevariable,withunknowndistributionfullctioll(d.f.)F;Tistherandomlefttruncatiolltiliiewitharbitraryd.f.G,andYistherandorl.1rightcensoringtirliewitharbitraryd.f.H.ItisassumedthatX,T,Yarerxlutuallyindependent.Intheran相似文献   

左截断右删失下一类泛函估计的渐近性质

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截断与删失数据的极大似然估计的中偏差

本文研究了截断与删失模型,运用Taylor渐近展开方法,得到模型的极大似然估计的中偏差,比渐近正态性结果更加精细.     

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A KERNEL-TYPE ESTIMATOR OF A QUANTILE FUNCTION UNDER RANDOMLY TRUNCATED DATA

A kernel-type estimator of the quantile function Q(p) = inf {t : F(t)≥p}, 0≤p≤1, is proposed based on the kernel smoother when the data are subjected to random truncation. The Bahadur-type representations of the kernel smooth estimator are established, and from Bahadur representations the authors can show that this estimator is strongly consistent, asymptotically normal, and weakly convergent.     

一类纵向数据半参数模型估计的收敛速度

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混合von Mises 模型的参数估计

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DISTRIBUTION FREE LAWS OF THE ITERATED LOGARITHM FOR KERNEL ESTIMATOR OF REGRESSION FUNCTION BASED ON DIRECTIONAL DATA DISTRIBUTION FREE LAWS OF THE ITERATED LOGARITHM FOR KERNEL ESTIMATOR OF REGRESSION FUNCTION BASED ON DIRECTIONAL DATA

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矩阵损失函数下参数估计的几个结果

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Modal Regression Based on Nonparametric Quantile Estimator

Modal regression based on nonparametric quantile estimator is given. Unlike the traditional mean and median regression, modal regression uses mode but not mean or median to represent the center of a conditional distribution, which helps the model to be more robust for outliers, asymmetric or heavy-taileddistribution. Most of solutions for modal regression are based on kernel estimation of density. This paper studies a new solution for modal regression by means of nonparametric quantile estimator. This method builds on the fact that the distribution function is the inverse of the quantile function, then the flexibility of nonparametric quantile estimator is utilized to improve the estimation of modal function. The simulations and application show that the new model outperforms the modal regression model via linear quantile function estimation.     

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