带有辅助信息的ROC曲线两种估计的比较

在医学研究中,常常使用受试者操作特性曲线(ROC)曲线来研究两样本的比较问题。Lloyd构造了ROC曲线的核平滑估计,并给出了其渐近偏差以及渐近标准差。此外,当还可以获悉某一处理组上的辅助信息时,Zhou,Zhou & Ma利用经验似然的方法构造了ROC曲线的核平滑经验似然估计。本文利用"亏量"这个概念比较了带有辅助信息的情况下,对核平滑经验似然估计与完全经验似然估计进行了比较。并给出了核平滑经验似然估计优于完全经验似然估计的结论,并且随着样本容量的增大,该亏量也是无限增大的。     

左截断右删失数据分位差估计及其渐近性质

我们研究了左截断右删失数据分位差,基于左截断右删失数据乘积限构造了分位差的经验估计,同时克服经验估计的非光滑性,提出了分位数差的核光滑估计.利用经验过程理论推导出这两个估计的渐近偏差和渐近方差,并且在左截断右删失数据下研究了这两个分位差的大样本性质,获得分位差估计的相合性和渐近正态性.同时给出计算模拟以验证光滑分位差估计的表现,在均方损失的意义下模拟结果表明光滑估计比经验估计具有更好的性质.     

Relative deficiency of quantile estimators for left truncated and right censored data

The quantity deficiency which was proposed by Hodges and Lehmann (1970) is used to compare different statistical procedures. In this article, the deficiency of the sample quantile estimator with respect to the kernel quantile estimator for left truncated and right censored (LTRC) data in the sense of Hodges and Lehmann is considered. We also give the optimal bandwidth for the kernel quantile estimator. Monte Carlo studies are conducted to illustrate our results.     

金融风险管理中VaR度量的光滑估计效率

本文研究了金融风险管理理论中风险价值(VaR)的非参数核光滑估计和经验估计的效率问题.对非独立的时间序列损失/收益样本,在均方误差(MSE)准则的意义下引入亏量的概念,亏量越大表明估计效率越低.并利用亏量对VaR模型的核光滑估计和基于样本分位数的经验估计进行了比较,在理论上证明了VaR模型的核光滑估计优于经验估计.同时,通过计算机模拟证实了理论获得的结论.本文还对国内沪深两市上的证券投资基金进行了实证分析,计算了样本基金的VaR风险度量的经验估计和核光滑估计,并计算了样本基金基于周收益率和VaR估计的风险调整收益(RAROC)值,以此对样本基金的业绩做出了有用的评价.     

Nonparametric regression with interval-censored data

In many medical studies,the prevalence of interval censored data is increasing due to periodic monitoring of the progression status of a disease.In nonparametric regression model,when the response variable is subjected to interval-censoring,the regression function could not be estimated by traditional methods directly.With the censored data,we construct a new response variable which has the same conditional expectation as the original one.Based on the new variable,we get a nearest neighbor estimator of the regression function.It is established that the estimator has strong consistency and asymptotic normality.The relevant simulation reports are given.     

Life expectancy under random censorship

This paper deals with estimation of life expectancy used in survival analysis and competing risk study under the condition that the data are randomly censored by K independent censoring variables. The estimator constructed is based on a theorem due to Berman [2], and it involves an empirical distribution function which is related to the Kaplan-Meier estimate used in biometry. It is shown that the estimator, considered as a function of age, converges weakly to a Gaussian process. It is found that for the estimator to have finite limiting variance requires the assumption that the censoring variables be stochastically larger than the “survival” random variable under investigation.     

单参数指数分布产品截尾样本场合简单步进应力加速寿命试验损伤失效率模型下的统计分析

本文给出单参数指数分布产品截尾样本场合简单步进应力加速寿命试验损伤失效率模型下参数的极大似然估计和拟矩估计。     

随机删失非参数回归模型的离差度量

主要研究了随机删失非参数固定设计回归模型的离差度量.首先利用随机删失非参数回归模型的性质和生存分布的Kaplan-Meier乘积限估计,将原模型转化为非参数回归模型进行研究;然后对模型作了离差度量分析,得到了模型的离差度量序列;最后通过实例分析,验证了上述诊断方法的有效性.     

Model-based likelihood ratio confidence intervals for survival functions

We introduce an adjusted likelihood ratio procedure for computing pointwise confidence intervals for survival functions from censored data. The test statistic, scaled by a ratio of two variance quantities, is shown to converge to a chi-squared distribution with one degree of freedom. The confidence intervals are seen to be a neighborhood of a semiparametric survival function estimator and are shown to have correct empirical coverage. Numerical studies also indicate that the proposed intervals have smaller estimated mean lengths in comparison to the ones that are produced as a neighborhood of the Kaplan-Meier estimator. We illustrate our method using a lung cancer data set.     

Kullback-Leibler Information Consistent Estimation for Censored Data

This paper is intended as an investigation of parametric estimation for the randomly right censored data. In parametric estimation, the Kullback-Leibler information is used as a measure of the divergence of a true distribution generating a data relative to a distribution in an assumed parametric model M. When the data is uncensored, maximum likelihood estimator (MLE) is a consistent estimator of minimizing the Kullback-Leibler information, even if the assumed model M does not contain the true distribution. We call this property minimum Kullback-Leibler information consistency (MKLI-consistency). However, the MLE obtained by maximizing the likelihood function based on the censored data is not MKLI-consistent. As an alternative to the MLE, Oakes (1986, Biometrics, 42, 177–182) proposed an estimator termed approximate maximum likelihood estimator (AMLE) due to its computational advantage and potential for robustness. We show MKLI-consistency and asymptotic normality of the AMLE under the misspecification of the parametric model. In a simulation study, we investigate mean square errors of these two estimators and an estimator which is obtained by treating a jackknife corrected Kaplan-Meier integral as the log-likelihood. On the basis of the simulation results and the asymptotic results, we discuss comparison among these estimators. We also derive information criteria for the MLE and the AMLE under censorship, and which can be used not only for selecting models but also for selecting estimation procedures.     

Bezier curve smoothing of the Kaplan-Meier estimator

Estimation of a survival function from randomly censored data is very important in survival analysis. The Kaplan-Meier estimator is a very popular choice, and kernel smoothing is a simple way of obtaining a smooth estimator. In this paper, we propose a new smooth version of the Kaplan-Meier estimator using a Bezier curve. We show that the proposed estimator is strongly consistent. Numerical results reveal the that proposed estimator outperforms the Kaplan-Meier estimator and its kernel weighted smooth version in the sense of mean integrated square error. This research is supported by the Korea Research Foundation (1998-015-d00047) made in the program year of 1998.     

响应变量删失情况下线性模型的FIC模型选择和模型平均

本文给出了响应变量随机右删失情况下线性模型的FIC (focused information criterion) 模型选择方法和光滑FIC 模型平均估计方法, 证明了兴趣参数的FIC 模型选择估计和光滑FIC 模型平均估计的渐近正态性, 通过随机模拟研究了估计的有限样本性质, 模拟结果显示, 从均方误差和一定置信水平置信区间的经验覆盖概率看, 兴趣参数的光滑FIC 模型平均估计均优于FIC, AIC (Akaikeinformation criterion) 和BIC (Bayesian information citerion) 等模型选择估计; 而FIC 模型选择估计与AIC 和BIC 等模型选择估计相比, 也表现出了一定的优越性. 通过分析原发性胆汁性肝硬化数据集, 说明了本文方法在实际问题中的应用.     

生存分析中乘积限估计的大样本性质

生存分析中，人们关心的问题之一是利用不完全的寿命调查数据估计生物折寿命分布。在实际问题中，比较常见的不完全数据包括右删失数据，左截断数据和左截断右删失数据。利用这三种数据估计寿命分布时，常用的统计量是乘积限估计。于是，乘积限估计的大样本性质的研究一直受到关注。本文就这方面的研究近况做一比较系统的论述。     

Asymptotic normality of wavelet density estimator under censored dependent observations

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 ??-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.     

Empirical likelihood analysis of the Buckley-James estimator

The censored linear regression model, also referred to as the accelerated failure time (AFT) model when the logarithm of the survival time is used as the response variable, is widely seen as an alternative to the popular Cox model when the assumption of proportional hazards is questionable. Buckley and James [Linear regression with censored data, Biometrika 66 (1979) 429-436] extended the least squares estimator to the semiparametric censored linear regression model in which the error distribution is completely unspecified. The Buckley-James estimator performs well in many simulation studies and examples. The direct interpretation of the AFT model is also more attractive than the Cox model, as Cox has pointed out, in practical situations. However, the application of the Buckley-James estimation was limited in practice mainly due to its illusive variance. In this paper, we use the empirical likelihood method to derive a new test and confidence interval based on the Buckley-James estimator of the regression coefficient. A standard chi-square distribution is used to calculate the P-value and the confidence interval. The proposed empirical likelihood method does not involve variance estimation. It also shows much better small sample performance than some existing methods in our simulation studies.     

A Dvoretzky–Kiefer–Wolfowitz type inequality for the Kaplan–Meier estimator

We prove a new exponential inequality for the Kaplan–Meier estimator of a distribution function in a right censored data model. This inequality is of the same type as the Dvoretzky–Kiefer–Wolfowitz inequality for the empirical distribution function in the non-censored case. Our approach is based on Duhamel equation which allows to use empirical process theory.     

几何分布截尾样本场合步进应力加速寿命试验TFR模型下的统计分析

讨论了几何分布产品在步进应力加速试验TFR模型下寿命分布.给出了其寿命分布函数步进形式,在截尾样本场合利用极大似然估计方法和拟矩估计方法求出了未知参数的点估计,最后利用计算机模拟考察了说明本文方法的可行性.     

随机删失下经验贝叶斯估计的渐近最优性

该文运用经验贝叶斯(empirical Bayes(简称EB))方法,在历史样本和当前样本均被另一个具有未知分布的变量随机右删失的条件下,构造了一个指数分布参数的经验贝叶斯估计并获得了它的渐近最优性．文章最后给出了一个例子和模拟结果．     

Smoothed jackknife empirical likelihood method for ROC curve

In this paper we propose a smoothed jackknife empirical likelihood method to construct confidence intervals for the receiver operating characteristic (ROC) curve. By applying the standard empirical likelihood method for a mean to the jackknife sample, the empirical likelihood ratio statistic can be calculated by simply solving a single equation. Therefore, this procedure is easy to implement. Wilks’ theorem for the empirical likelihood ratio statistic is proved and a simulation study is conducted to compare the performance of the proposed method with other methods.