多元失效时间数据的一般边际半参数危险率模型研究

在生物医学研究中,多元失效时间数据非常常见.该文提出用一般边际半参数危险率回归模型来分析多元失效时间数据.此模型包括了三种常用边际模型:边际比例风险模型、边际加速失效时间模型和边际加速危险模型作为子模型.对于模型中的回归系数,可以通过估计方程的方法来估计它,同时也给出了基准累积危险率函数的估计.得到的估计可以证明是相合的和渐近正态的.     

多类型复发事件间隔时间下可加危险率模型

在许多的生物医学和工程研究中,多类型复发事件的间隔时间数据是很常见的。众所周知,比例危险率模型在一些情况下不能很好拟合生存数据。本文,在多类型复发事件的间隔时间数据下,我们利用可加危险率模型来研究协变量对生存时间的影响程度。我们采用估计方程方法获得回归系数和基准累积危险率函数估计。并且,我们建立了所提估计的渐近分布。     

半参数ACD模型及其在中国股票市场中的应用研究

本文提出了半参数ACD模型并基于模拟样本与调整后的中国股票市场的价格时间间隔样本对模型进行实证分析.半参数ACD模型对条件期望的函数形式与随机误差项的分布形式要求都没有参数ACD模型强,因此不会像参数ACD模型那样因模型形式设定错误而得出错误结论.这一点在我们的实证分析中可以得到证实.与非参数ACD模型相比,半参数ACD模型能够估计出参数,这增加了模型的解释能力.半参数ACD模型估计出来的各个可加部分图形的形状对于正确设定参数ACD模型具有一定的指导作用.     

可加风险模型下相依Ⅰ型区间删失数据的一个Copula推断方法

可加风险模型是生存分析中一类重要的回归模型,许多学者对该模型进行过研究.但是针对相依Ⅰ型区间删失数据的研究却非常少,且已有的研究都假设删失时间与寿命之间的关联系数已知.显然,该假设在实际中未必成立.针对此问题,本文放松这一假设,提出一种新的基于Copula的方法对可加风险模型下相依Ⅰ型区间删失数据进行回归分析,给出参数部分估计量的渐近性质,通过数值模拟检验所提方法在有限样本下的表现,并进行实例分析.     

多类型复发事件下变系数加性乘积比率回归模型

本文基于多类型复发事件数据,讨论了一个新的加性乘积比率回归模型,该模型包括两部分,其中第一部分为可加Aalen模型,其中协变量影响为加性的且与时间有关.第二部分为Cox回归模型,其中协变量有乘性影响.利用估计方程的方法,给出了该模型中未知参数和非参数函数的一种估计方法,并利用现代经验过程理沦证明了所得估计的相合性和渐近正态性.     

多指标可加模型及在医疗费用预测中的应用

对医疗费用的建模分析与合理预测是医疗保险费用厘定的基础与根本.医疗费用中的高维附加信息在长期预测中具有重要作用.然而,传统的统计建模方法不适用于处理高维纵向数据下的医疗费用.本文提出部分线性多指标可加模型,对具有高维特征的纵向医疗费用数据进行拟合与预测,并且使用两种不同的降维估计方法进行模型估计,并将该模型应用于一组含...     

基于右删失宽相依数据的Kaplan-Meier估计和风险率估计的渐近性质

本文在生存时间和删失时间均为宽相依数据下,建立了生存函数的Kaplan-Meier估计和风险率估计的强逼近和强表示,获得的强逼近和强表示误差项的收敛速度达到O(n~(-1/2)log~(1/2)n).所得结果推广了负相协和负超可加相依数据情形下的相关结果.     

基于众数回归的部分函数型线性可加模型的稳健估计

本文讨论部分函数型线性可加模型参数的稳健估计,该模型由经典的可加回归模型和函数型线性模型组合而成.采用B-样条基函数对模型中斜率函数和非参数可加函数进行近似,然后通过最大化众数回归目标函数得到基于众数回归的估计.在一些正则条件下,本文给出估计的收敛速度和渐近分布.最后通过模拟计算和应用实例以表明所提方法的有效性.模拟结果表明,该方法不仅具有稳健性,即不易受污染数据或厚尾分布的影响,而且在信噪比较大时可以与最小二乘方法有相同的表现.     

基于加速失效时间模型模拟生存时间应用于检测生存性状位点

对于考察预指定情形下的统计模型的性能、性质及适应性,模拟研究是非常重要的统计工具.作为生存分析中两个最受欢迎的模型之一,由于加速失效时间模型中的因变量是生存时间的对数,且此模型能够以线性形式回归带有易解释的参数的协变量,从而加速失效模型比COX比例风险模型更便于拟合生存数据.首先提出了关于带有广义F-分布的加速失效模型的模拟研究中生成生存时间的方法,然后给出了描述加速失效时间模型的误差分布和相应的生存时间之间的一般的关系式,并给出了广义F-分布是如何生成生存时间的.最后,为证实所建议模拟技术的性能和有效性,将此方法应用于检测生存性状位点的模型中.     

可加模型的无交叉分位回归曲线与房价问题研究

高维数据分析是当前研究的热点话题,而在对其进行分析时,非参数方法由于其灵活,无需对模型进行假定,得到了广泛的发展和认可。其中可加模型不仅能够有效地对变量进行降维,避免"维数灾难"的发生;而且能够得到各个变量的边际效应,具有很好的解释性。为了得到更加稳健的估计量,本文考虑利用分位回归方法对可加模型进行估计。分位回归方法由于其能够全面地刻画因变量在各个分位点上的变化趋势,并不受误差分布的限制,使得该方法具有更广泛的应用性。本文综合考虑以上优势,提出局部线性最小化检验函数估计方法和局部线性双核估计方法对可加模型进行估计。并且该方法能够有效地避免可加模型分位回归曲线的交叉问题.蒙特卡洛结果显示,与传统的均值估计法相比,不论误差分布的形式,我们提出的方法更具有优越性。用北京市二手房房价数据进行实证分析,进一步验证了本文提出的估计方法。     

Measurement error in proportional hazards models for survival data with long-term survivors

This work studies a proportional hazards model for survival data with "long-term survivors",in which covariates are subject to linear measurement error.It is well known that the naive estimators from both partial and full likelihood methods are inconsistent under this measurement error model.For measurement error models,methods of unbiased estimating function and corrected likelihood have been proposed in the literature.In this paper,we apply the corrected partial and full likelihood approaches to estimate the model and obtain statistical inference from survival data with long-term survivors.The asymptotic properties of the estimators are established.Simulation results illustrate that the proposed approaches provide useful tools for the models considered.     

The Additive-multiplicative Hazards Mo del for Multiple Typ e of Recurrent Gap Times

Recurrent event gap times data frequently arise in biomedical studies and often more than one type of event is of interest. To evaluate the effects of covariates on the marginal recurrent event hazards...     

Multivariate Survival Models with a Mixture of Positive Stable Frailties

In this paper, we describe models for dependent multivariate survival data using finite mixtures of positive stable frailty distributions. We investigate the cross-ratio function as a local measure of association. We estimate the parameters in the stable mixture together with the parameters of the (conditional) proportional hazards model in a Bayesian framework using Markov chain Monte Carlo algorithms. We illustrate the methodology using data on kidney infections.     

基于病例队列数据的比例风险模型的诊断

病例队列设计是一种在生存分析中广泛应用的可以降低成本又能提高效率的抽样方法.对于病例队列数据,已经有很多统计方法基于比例风险模型来估计协变量对生存时间的影响.然而,很少有工作基于病例队列数据来检验模型的假设是否成立.在这篇文章中,我们基于渐近的零均的值随机过程提出了一类检验统计量,这类检验统计量可以基于病例队列数据来检验比例风险模型的假设是否成立.我们通过重抽样的方法来逼近上述检验统计量的渐近分布,通过数值模拟来研究所提方法在有限样本下的表现,最后将所提出的方法应用于一个国家肾母细胞瘤研究的真实数据集上.     

Time to default in credit scoring using survival analysis: a benchmark study

We investigate the performance of various survival analysis techniques applied to ten actual credit data sets from Belgian and UK financial institutions. In the comparison we consider classical survival analysis techniques, namely the accelerated failure time models and Cox proportional hazards regression models, as well as Cox proportional hazards regression models with splines in the hazard function. Mixture cure models for single and multiple events were more recently introduced in the credit risk context. The performance of these models is evaluated using both a statistical evaluation and an economic approach through the use of annuity theory. It is found that spline-based methods and the single event mixture cure model perform well in the credit risk context.     

Maximum likelihood estimation in the proportional hazards cure model

The proportional hazards cure model generalizes Cox’s proportional hazards model which allows that a proportion of study subjects may never experience the event of interest. Here nonparametric maximum likelihood approach is proposed to estimating the cumulative hazard and the regression parameters. The asymptotic properties of the resulting estimators are established using the modern empirical process theory. And the estimators for the regression parameters are shown to be semiparametric efficient.     

Efficient Algorithm for Computing Maximum Likelihood Estimates in Linear Transformation Models

Linear transformation models, which have been extensively studied in survival analysis, include the two special cases: the proportional hazards model and the proportional odds model. Nonparametric maximum likelihood estimation is usually used to derive the efficient estimators. However, due to the large number of nuisance parameters, calculation of the nonparametric maximum likelihood estimator is difficult in practice, except for the proportional hazards model. We propose an efficient algorithm for computing the maximum likelihood estimates, where the dimensionality of the parameter space is dramatically reduced so that only a finite number of equations need to be solved. Moreover, the asymptotic variance is automatically estimated in the computing procedure. Extensive simulation studies indicate that the proposed algorithm works very well for linear transformation models. A real example is presented for an illustration of the new methodology.     

左截断双删失数据下可加可乘风险率模型的估计

在生存分析中,可加可乘风险率模型常用来研究协变量对初始事件和终止事件之间持续时间的影响效应。在本文中,我们考虑了在初始事件存在部分区间删失,同时终止事件存在左截断右删失的情形下,持续时间的可加可乘风险率模型的估计问题。我们提出了一个两阶段估计过程来估计模型的回归参数。并通过模拟分析验证了估计的大样本性质。最后利用该方法分析了恶性黑色素瘤手术治疗数据。     

A general transformation class of semiparametric cure rate frailty models

We consider a class of cure rate frailty models for multivariate failure time data with a survival fraction. This class is formulated through a transformation on the unknown population survival function. It incorporates random effects to account for the underlying correlation, and includes the mixture cure model and the proportional hazards cure model as two special cases. We develop efficient likelihood-based estimation and inference procedures. We show that the nonparametric maximum likelihood estimators for the parameters of these models are consistent and asymptotically normal, and that the limiting variances achieve the semiparametric efficiency bounds. Simulation studies demonstrate that the proposed methods perform well in finite samples. We provide an application of the proposed methods to the data of the age at onset of alcohol dependence, from the Collaborative Study on the Genetics of Alcoholism.     

Conditional laplace transforms for bayesian nonparametric inference in reliabity theory

In order to apply nonparametric meyhods to reliability problems, it is desibrable to have available priors over a broad class of survival distributions.In the paper, this is achieved by taking the failure rate function to be the sum oof a nonnegative stochastic process with increasing sample patjhs and a process with decreasing sample paths. This approach produces a prior which chooses an absolutely survival distribution that can have an IFR, DFR, or U-shapped failure rate. Posterior Laplace transforms of the failure rate are obtained based on survival data allows censoring. Bayes estimates of the failure rate as well as the lifetime distribution are then calculated from these posterior Laplace transforms. This approach is also applied to a competing risks model and the proportional hazards model of Cox.