纵向数据下半参数回归模型估计的渐近性质

本文考虑纵向数据下半参数回归模型: $y_{ij}=x_{ij}'\beta+g(t_{ij})+e_ij},\;i=1,\cdots,m,\;j=1,\cdots,n_i$. 基于最小二乘法和一般的非参数权函数方法给出了模型中参数$\beta$和回归函数$g(\cdot)$的估计, 并在适当条件下证明了$\beta$估计量的渐近正态性和$g(\cdot)$估计量的最优收敛速度\bd 模拟结果表明我们的估计方法在有限样本情形有良好的效果     

在区间截断的情况下,两点分布估计及其收敛速度

假设总体$X$服从两点均匀分布, 即$\pr(X=x_1)=\pr(X=x_2)=1/2$, 但是随机变量$X$的取值$x_1$和$x_2$是未知的\bd 在区间截断的情况下, 利用样本获得了$x_1$和$x_2$估计量$\wh{x}_1$和$\wh{x}_2$, 并给出了估计量$\wh{x}_1$和$\wh{x}_2$的收敛速度$o(n^{-1/3+\xs})$.     

方差分量模型中回归系数的线性估计的可容许性

考虑方差分量模型$\ep Y=X\beta,\;\cov(Y)=\tsm_{i=1}^{m}\theta_iV_i$, 其中$n\times p$矩阵$X$和非负定矩阵$V_i\;(i=1,2,\cdots,m)$都是已知的, $\beta\in R^p,\;\theta_i\geq 0$或$\theta_i>0\;(i=1,2,\cdots,m)$均为参数\bd 在本文中, 我们在二次损失下, 当$\mu{(X)} \subset\mu{(V)}$时, 给出了关于可估函数$S\beta$的线性估计在线性估计类中可容许性的充要条件     

不完全椭球约束下多元线性模型线性估计的可容许性

本文研究了多元线性模型当未知参数受不完全椭球约束$\mbox{tr}(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq\sigma^2$时线性估计的可容许性问题.具体而言,我们研究了约束$\mbox{tr}(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq\sigma^2$中$N$和非中心点$\Theta_1$对线性估计的可容许性的影响.主要结果表明在两个不同的不完全椭球约束条件$\mbox{tr}(\Theta-\Theta_1)'N(\Theta-\Theta_1)\leq\sigma^2$与$\mbox{tr}(\Theta-\Theta_2)'N(\Theta-\Theta_2)\leq\sigma^2$ 下,当$\Theta_1$和$\Theta_2$满足一定的关系时,可容许的齐次线性估计类是相同的.     

自正则化和Davis大数律和重对数律的精确渐近性

本文证明了自正则化Davis大数律和重对数律的精确渐近性, 即 {\heiti\bf 定理1}\hy 设$\ep X=0$, 且$\ep X^2I_{(|X|\leq x)}$在无穷远处是缓变函数, 则$\lim_{\varepsilon\searrow0}\varepsilon^2\tsm_{n\geq3}\frac{1}{n\log n}\pr\Big(\Big|\frac{S_n}{V_n}\Big|\geq\varepsilon\sqrt{\log\log n}\Big)=1.${\heiti\bf 定理2}\hy 设$\ep X=0$, 且$\ep X^2I_{(|X|\leq x)}$在无穷远处是缓变函数, 则对本文证明了目正则化Davis大数律和重对数律的精确渐近性,即定理1设EX=0,且EX~2I_(|x|≤x)在无穷远处是缓变函数,则■定理2设EX=0,且EX~2I_(|x|≤x)在无穷远处是缓变函数,则对0≤δ≤1,有■其中N为标准正态随机变量．     

区间数据参数估计的叠代法

本文讨论区间数据情况下, 指数分布参数的估计\bd 引入了两种叠代方法, 证明了在一定的条件下, 叠代过程的收敛性.     

在${\mathbb{R}}^m$中重对数广义律的精确速率

令\{$X$, $X_n$, $n\ge 1$\}是期望为${\mathbb{E}}X=(0,\ldots,0)_{m\times 1}$和协方差阵为${\rm Cov}(X,X)=\sigma^2I_m$的独立同分布的随机向量列, 记$S_n=\sum_{i=1}^{n}X_i$, $n\ge 1$. 对任意$d>0$和$a_n=o((\log\log n)^{-d})$, 本文研究了${{\mathbb{P}}(|S_n|\ge (\varepsilon+a_n)\sigma \sqrt{n}(\log\log n)^d)$的一类加权无穷级数的重对数广义律的精确速率.     

混合指数分布的参数估计

混合指数分布是寿命数据分析中一个非常重要的统计模型\bd 但是利用正规的统计方法如矩估计、极大似然估计等估计模型的参数往往比较困难\bd 本文应用EM算法详细研究了混合指数分布在正常工作条件下和在进行恒加应力加速寿命实验条件下, 在完全数据场合、I-型截尾和II-型截尾场合的参数估计问题\bd 模拟说明利用EM算法来估计混合指数分布是一种非常有效的方法.     

价值风险的非参数估计方法*

价值风险(VaR)模型是当今最流行的金融资产风险管理和控制的工具之一\bd 本文提出了用局部分位数回归的方法来估计某一投资组合的VaR值\bd 该方法可用于计算投资组合多持续期的VaR, 使得人们可以了解到该投资组合在一定持续期内的动态风险\bd 本文通过模拟和美国三个月到期国债利率数据的分析说明了该方法的具体执行情况, 并与J.P. Morgan的时间开方规则作了比较\bd 结果表明我们的VaR估计有令人满意的效果.     

监测L\'{e}vy稳定过程均值变点的平均运行时间估计

平均运行长度(时间) (ARL)是判断一控制图监测变点效果好坏的一个重要工具\bd 本文主要研究L\'{e}vy 稳定过程的均值变点监测问题\bd 我们给出了三个控制图, 即EWMA, GEWMA和GLR的ARL 估计, 并通过数值模拟比较了4个控制图监测均值变点的效果和差异.     

区间数据的均值估计

本文在运用无偏转换思想找到区间数据均值估计的基础上,对所找到的估计量的方差进行了研究.针对区间截断情况1和区间截断情况2,找到了估计量方差有限的条件.当截断随机变量的分布在某种程度上比被截断随机变量的分布尾部更厚时,方差有限的估计量可以取到.     

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In collecting clinical data, data would be censored due to competing risks or patient withdrawal. The statistical inference for censoring data is always based on the assumption that the failure time and censoring time is independent. But in practice the failure time and censoring time are often dependent. Dependent censoring make the job to deal with censoring data more complicated. In this paper, we assume that the joint distribution of the failure time variable and censoring time variable is a function of their marginal distributions. This function is called a copula. Under prespecified copulas, the maximum likelihood estimators for cox proportional hazards models are worked out. Statistical analysis results are carried by simulations. When dependent censoring happens, the proposed method will do better than the traditional method used in independent situations. Simulation results show that the proposed method can get efficient estimations.     

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.     

Random weighting method for Cox’s proportional hazards model

Variance of parameter estimate in Cox’s proportional hazards model is based on asymptotic variance. When sample size is small, variance can be estimated by bootstrap method. However, if censoring rate in a survival data set is high, bootstrap method may fail to work properly. This is because bootstrap samples may be even more heavily censored due to repeated sampling of the censored observations. This paper proposes a random weighting method for variance estimation and confidence interval estimation for proportional hazards model. This method, unlike the bootstrap method, does not lead to more severe censoring than the original sample does. Its large sample properties are studied and the consistency and asymptotic normality are proved under mild conditions. Simulation studies show that the random weighting method is not as sensitive to heavy censoring as bootstrap method is and can produce good variance estimates or confidence intervals.     

A New Approach to Censored Quantile Regression Estimation

Quantile regression provides an attractive tool to the analysis of censored responses, because the conditional quantile functions are often of direct interest in regression analysis, and moreover, the quantiles are often identifiable while the conditional mean functions are not. Existing methods of estimation for censored quantiles are mostly limited to singly left- or right-censored data, with some attempts made to extend the methods to doubly censored data. In this article, we propose a new and unified approach, based on a variation of the data augmentation algorithm, to censored quantile regression estimation. The proposed method adapts easily to different forms of censoring including doubly censored and interval censored data, and somewhat surprisingly, the resulting estimates improve on the performance of the best known estimators with singly censored data. Supplementary material for this article is available online.     

Consistency and Asymptotic Normality of Estimators in a Proportional Hazards Model with Interval Censoring and Left Truncation

Satten et al. (1998, J. Amer. Statist. Assoc., 93, 318–327) proposed an approach to the proportional hazards model for interval censored data in which parameter estimates are obtained by solving estimating equations which are the score equations for the full data proportional hazards model, averaged over all rankings of imputed failure times consistent with the observed censoring intervals. In this paper, we extend this approach to incorporate data that are left-truncated and right censored (dynamic cohort data). Consistency and asymptotic normality of the estimators obtained in this way are established.     

A novel means of estimating quantiles for 2-parameter Weibull distribution under the right random censoring model

Censoring models are frequently used in reliability analysis to reduce experimental time. The three types of censoring models are type-I, type-II and random censoring. In this study, we focus on the right random censoring model. In this model, if the failure time exceeds its associated censoring time, then the failure time becomes a censored observation. In this case, many authors (see: Lee, Statistical Methods for Survival Data Analysis, 2nd Edition, Wiley, New York, 1992; Lawless, Statistical Models and Methods for Lifetime Data, Wiley, New York, 1982; Miller, Survival Analysis, Wiley, New York, 1981, among others) considered using the observed censoring time to impute the censored observation which, however, underestimates the true failure time. Herein, two methods to impute the censored observations are proposed in a right random censoring model for a 2-parameter Weibull distribution. By a Monte Carlo simulation, the quantile estimates are calculated to assess the performance of the proposed imputation methods with respect to their relative mean square error. Simulation results indicate that the two imputation methods proposed herein are superior to the method proposed by the above authors if the shape parameter of Weibull distribution exceeds 1, except for the lower quantiles.     

The use of ROC for defining the validity of the prognostic index in censored data

The validity of a diagnostic marker can be summarised by using statistical measures either for the goodness of the fit like the deviance, measures of the explained variation like R² or the misclassification rate. Other intuitive measures are sensitivity and specificity in the case of binary response. In the absence of censored data the calculation of these measures is widely used. In the presence of censoring the estimation of time-dependent sensitivity and specificity is not well known. In this article we propose a new method for calculating ROC curves with censored data using the observed number of events and calculating the additional number of expected events for censored observations. The new method is illustrated with data for predicting mortality in patients surviving a myocardial infarction.     

STRONG UNIFORM CONSISTENCY FOR DENSITY ESTIMATOR FROM RANDOMLY CENSORED DATA

Let X_1,…,X_n be a sequence of independent identically distributed random variableswith distribution function F and density function f.The X_are censored on the right byY_i,where the Y_i are i.i.d.r.v.s with distribution function G and also independent of theX_i.One only observesLet S=1-F be survival function and S be the Kaplan-Meier estimator,i.e.,where Z_are the order statistics of Z_i and δ_((i))are the corresponping censoring indicatorfunctions.Define the density estimator of X_i by where =1-and h_n(>0)↓0.     

Weighted Empirical Likelihood Ratio Confidence Intervals for the Mean with Censored Data

We propose a procedure to construct the empirical likelihood ratio confidence interval for the mean using a resampling method. This approach leads to the definition of a likelihood function for censored data, called weighted empirical likelihood function. With the second order expansion of the log likelihood ratio, a weighted empirical likelihood ratio confidence interval for the mean is proposed and shown by simulation studies to have comparable coverage accuracy to alternative methods, including the nonparametric bootstrap-t. The procedures proposed here apply in a unified way to different types of censored data, such as right censored data, doubly censored data and interval censored data, and computationally more efficient than the bootstrap-t method. An example of a set of doubly censored breast cancer data is presented with the application of our methods.