双边删失场合生存函数的非参数Bayes估计的弱收敛性

本文针对双边删失数据讨论了生存函数的非参数Bayes估计问题,运用Fred-holm积分的一些经典结果,文中证明了该估计弱收敛的一个Gaussian过程。     

右删失数据下生存函数的随机加权估计

本文利用随机加权思想提出了右删失数据下生存函数的随机加权估计,证明了所给的随机加权估计的强相合性.     

随机删失数据下几种风险率函数估计的渐近性质

文中对于删失数据下几种不同的风险率函数估计进行了研究。使用与以往不同的方法，在较弱的条件下，改进并扩充了现有文献的结果，获得了这几种风险率函数估计的渐近正态性，一致强弱相合收敛速度以及重对数律且进行了数值模拟。     

删失数据下回归函数的最近邻估计

设（X，Y）是一随机向量且变量Y的均值存在．假定Y被另一分布G的随机变量t删失，仅能观察到不完全数据（xi，Yi^ti,δi），i＝1，2，…n，其中Yi^ti＝min（Yi，Ti）,δi=I（Yi≤ti）。为了给出回归函数m（x）＝E（Y|X）的估计。文中使用了Stute提出的最近邻型回归估计，并给出了该估计的强相合性结果．     

随机删失下回归函数最近邻估计的强收敛速度

针对随机右删失数据, 就截尾时间变量的分布已知和未知两种情况, 构造了一类非参数回归函数的最近邻估计, 在适当的条件下得到估计量的强收敛速度.     

删失数据密度函数估计及其强一致收敛速度

本文在假设被删失变量与删失变量之间不独立的情形下,给出了被删失变量的密度函数的核估计形式,得到了密度函数估计的强—致收敛速度．     

删失数据中删失指标随机缺失下回归函数的非参数估计

本文在删失数据中删失指标随机缺失的情况下,运用非参数方法给出了回归函数的两种估计量,给出了估计量的一致收敛速度以及渐近分布,并进一步通过数值模拟验证了所提方法在有限样本下的性质.     

删失数据下医药费用的经验似然估计

生存时间数据删失问题是医药费用统计推断的一大难点,本文针对可以获得费用历史信息的情形,提出了在删失数据下医药费用的经验似然估计方法,给出在一定置信水平下感兴趣参数的置信区间.通过模拟,比较了在不同样本量下本文提出的方法与已有方法的表现,结果是我们的方法得到的置信区间更短,估计效果更好.同时,我们也针对一项心脏病实验的实际数据进行了相关分析和比较,得到满意的结果.     

删失回归模型LAD估计的一些强的性质

删失回归模型是一种因变量受限制的模型, 也称为Tobit模型. 在较弱的条件之下, 给出了回归系数LAD估计的强相合性、强收敛速度及其Bahadur强表示.     

Existence theorems of a maximum likelihood estimate from a generalized censored data sample

Summary A new type of random sample, called a generalized censored data sample, is defined. An approach to finding criteria for the existence of a maximum likelihood estiamte from a finite generalized censored data sample is presented. This approach, named the probability contents boundary analysis, gives systematically a number of practical criteria, each of which is effective for various kinds of typical distribution families in statistical analysis.     

A class of estimators of the mean survival time from interval censored data with application to linear regression

A class of estimators of the mean survival time with interval censored data are studied by unbiased transformation method. The estimators are constructed based on the observations to ensure unbiasedness in the sense that the estimators in a certain class have the same expectation as the mean survival time. The estimators have good properties such as strong consistency （with the rate of O（n^-1/1 （log log n）^1/2）） and asymptotic normality. The application to linear regression is considered and the simulation reports are given.     

Strong convergence of LAD estimates in a censored regression model

A regression model with a nonnegativity constraint on the dependent variable, known as censored median regression model, is considered. Under some mild conditions, the LAD estimate of the regression coefficient is shown to be strongly consistent. Furthermore, its convergence rate and Bahadur strong representation are also obtained.     

Kernel estimators of the ROC Curve with censored data

Receiver operating characteristic (ROC) curves are often used to study the two sample problem in medical studies. However, most data in medical studies are censored. Usually a natural estimator is based on the Kaplan-Meier estimator. In this paper we propose a smoothed estimator based on kernel techniques for the ROC curve with censored data. The large sample properties of the smoothed estimator are established. Moreover, deficiency is considered in order to compare the proposed smoothed estimator of the ROC curve with the empirical one based on Kaplan-Meier estimator. It is shown that the smoothed estimator outperforms the direct empirical estimator based on the Kaplan-Meier estimator under the criterion of deficiency. A simulation study is also conducted and a real data is analyzed.     

Asymptotic theory of nonparametric regression estimates with censored data

For regression analysis, some useful information may have been lost when the responses are right censored. To estimate nonparametric functions, several estimates based on censored data have been proposed and their consistency and convergence rates have been studied in literature, but the optimal rates of global convergence have not been obtained yet. Because of the possible information loss, one may think that it is impossible for an estimate based on censored data to achieve the optimal rates of global convergence for nonparametric regression, which were established by Stone based on complete data. This paper constructs a regression spline estimate of a general nonparametric regression function based on right_censored response data, and proves, under some regularity conditions, that this estimate achieves the optimal rates of global convergence for nonparametric regression. Since the parameters for the nonparametric regression estimate have to be chosen based on a data driven criterion, we also obtain the asymptotic optimality of AIC, AICC, GCV, Cp and FPE criteria in the process of selecting the parameters.     

区间数据情形下线性模型的经验似然推断

§1Introduction Instatisticalapplications,weoftenencounterintervalcensoreddatawhenafailure timeYcannotbeobserved,butcanonlybedeterminedtolieinanintervalobtainedfroma sequenceofexaminationtimes.Forinstance,themaximumdosagewhichpatientscan endureisconcerned.LetYibethemaximumdosagewhichtheithpatientcanendure,Ui,j(j=1,2,...,k)bethedosagewhichthepatienthasbeentested.ItisobviousthatYiis unobservable.SupposetheithpatientisnormalwhenthedosageisUi,j,andhe(orshe)is abnormalwhenthedosageisUi,j+1.Then…     

A Strong Representation of the Product-Limit Estimator for Left Truncated and Right Censored Data

In this paper we consider the TJW product-limit estimatorF_n(x) of an unknown distribution functionFwhen the data are subject to random left truncation and right censorship. An almost sure representation of PL-estimatorF_n(x) is derived with an improved error bound under some weaker assumptions. We obtain the strong approximation ofF_n(x)−F(x) by Gaussian processes and the functional law of the iterated logarithm is proved for maximal derivation of the product-limit estimator toF. A sharp rate of convergence theorem concerning the smoothed TJW product-limit estimator is obtained. Asymptotic properties of kernel estimators of density function based on TJW product-limit estimator is given.     

截断情况下生存函数的函数的经验似然

Owen首次在完全样本下提出了经验似然的方法,WangQihua将该方法应用到带有截断情况的生存函数的函数估计问题．本文给出了更为一般的调整似然比统计量,证明了在适当的情况下该统计量仍渐近服从χ^2分布,同时模拟的结果也表明该统计量具有良好的性质。     

LAD estimation for nonlinear regression models with randomly censored data

The least absolute deviations (LAD) estimation for nonlinear regression models with randomly censored data is studied and the asymptotic properties of LAD estimators such as consistency, boundedness in probability and asymptotic normality are established. Simulation results show that for the problems with censored data, LAD estimation performs much more robustly than the least squares estimation.     

Nonparametric estimators of the survival function with twice censored data

Patilea and Rolin (Ann Stat 34(2):925–938, 2006) proposed a product-limit estimator of the survival function for twice censored data. In this article, based on a modified self-consistent (MSC) approach, we propose an alternative estimator, the MSC estimator. The asymptotic properties of the MSC estimator are derived. A simulation study is conducted to compare the performance between the two estimators. Simulation results indicate that the MSC estimator outperforms the product-limit estimator and its advantage over the product-limit estimator can be very significant when right censoring is heavy.