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.     

带相依终止事件的复发事件数据的可加可乘比率模型

本文对带相依终止事件的复发事件数据提出了一个联合建模分析方法,用一个带脆弱变量的可加可乘比率模型来刻画复发事件过程,还用带脆弱变量的Cox风险率模型来刻画终止事件过程,而且这两个过程的相依性由脆弱变量来刻画.我们利用估计方程的方法,对模型参数进行了估计,给出了所得估计的渐近性质.同时,通过数值模拟分析验证了估计的渐近性质.最后,利用该方法分析了弗吉尼亚大学慢性心脏病病人医疗诊费数据.     

带信息终止事件的复发事件数据的联合建模分析

在本文中,我们利用联合建模方法,分析了带有信息终止事件的复发事件数据.对复发事件过程和终止事件过程,我们分别构造了可加比率和风险率模型,并引入一个共用的不可观测的脆弱变量刻画复发事件过程与终止事件过程的相依性.利用广义估计方程的方法,对模型参数进行了估计;并给出了所得估计的大样本性质.     

复发事件下一类加性乘性转移模型p阶矩指数稳定性北大核心CSCD

复发事件数据频繁的出现在纵向研究中,本文基于生物医学中的单类型复发事件数据,提出了一类加性乘性转移模型,该模型包含了一些重要的半参数模型.同时,模型允许协变量具有加性和乘性的影响,且加性影响随时间而变化.利用广义估计方程的思想,对模型中未知参数和非参数函数进行了估计,并证明了所得估计的相合性和渐近正态性.最后,用数值模拟的方法验证了所提估计的可行性.     

多类型复发事件下的一类均值加速回归模型

在复发事件的统计分析中,事件的平均发生个数可能比其强度函数或危险率函数更具可解释性.为了评价协变量对复发事件的影响,许多学者考虑了复发事件的边际比例均值模型.然而,在许多实际应用中,协变量对复发事件不仅具有均值比例效应,而且还可能会加速或减缓复发事件的发生,即协变量对复发事件均值过程具有时间尺度效应.在多类型复发事件数据框架下,考虑一类广泛的加速均值模型.利用估计方程方法,获得了该模型中未知参数的估计,并且建立了所给估计的渐近性质.进一步通过模拟研究证实所提方法的优良表现.     

《生存数据分析的统计方法》

书评 :生存数据泛指涉及一定事件的时间数据。事件可以是生命死亡、疾病的发生、产品的失效、一种处理的反应等等。生存数据除生存时间准确知道的完全数据外 ,更多的是在研究结束时 ,某些个体还没有出现所关心的事件 ,这些个体的确切生存时间是不知道的 ,即数据是删失的。生存数据分析已经成为现代数理统计学的一个重要分支。L ee,E.T.编著 ,陈家鼎、戴中维翻译的《生存数据分析的统计方法》是这一领域一本优秀的专著。该书对读者的数学知识要求不高 ,有基本的概率论和数理统计知识和一些代数、微积分训练即可。因而它不但可作为统计专业…     

混合复发事件与面板数据下的可加可乘模型

事件历史记录数据(Event History Data)是随时间观察而得到的,记录特定事件发生时间和发生类型的观测数据.这种数据类型在生物医学等领域的研究中十分常见,它包含了两类非常重要的数据类型,复发事件数据(Recurrent Event Data)和面板计数数据(Panel Count Data).在实际生产过程中,有时会出现上述两种数据类型混杂的情况,文本提出了可加可乘半参数建模的方法来分析这种混杂数据.我们讨论了参数估计的相合性和渐近正态性,以及基准率函数的渐近高斯性质.我们进行了数据模拟,比较了我们提出的方法与naive方法的区别.     

带终止事件的多类型复发事件的一般加性乘积比例模型

带终止事件(例如死亡)的复发事件经常出现在医疗和日常观测中,大多数模型假定协变量效应是乘性的,并在给定生存分布条件下对事件复发率建模.在本文中,我们对带终止事件的复发事件数据建立一般加性乘积比例模型,这里所谓的终止事件就是阻止复发事件的再发生.利用估计方程法和逆概率加权方法,我们分别提出两种估计回归参数和基本均值函数的方法.并且建立了估计的渐近性质.     

右删失数据下多响应AFT模型的两阶段估计

在生存分析领域,加速失效时间(AFT)模型经常被用于预测事件发生的时间.本文将该模型推广到多事件时间情形,提出了多响应AFT模型,并假设协变量是高维的,模型的系数矩阵是联合低秩且稀疏的.此外还假设多个事件时间受制于同一个右删失变量.为了估计模型中的系数矩阵,本文提出一个两阶段方法,先对数据进行逆概率删失加权(IPCW),再用SESS算法求解一个稀疏降秩回归问题.本文通过数值模拟,验证了所提方法的有效性.最后将该方法应用于一个关于白血病患者骨髓移植的临床数据集.     

复发事件下一类加性乘性转移模型p阶矩指数稳定性

复发事件数据频繁的出现在纵向研究中,本文基于生物医学中的单类型复发事件数据,提出了一类加性乘性转移模型,该模型包含了一些重要的半参数模型.同时,模型允许协变量具有加性和乘性的影响,且加性影响随时间而变化.利用广义估计方程的思想,对模型中未知参数和非参数函数进行了估计,并证明了所得估计的相合性和渐近正态性.最后,用数值模拟的方法验证了所提估计的可行性.     

From a Floyd-Auslander minimal system to an odd triangular map

In 1989 A.N. Sharkovsky asked the question which of the properties characterizing continuous maps of the interval with zero topological entropy remain equivalent for triangular maps of the square. The problem is difficult and only partial results are known. However, in the case of triangular maps with nondecreasing fibres there are only few gaps in a classification (given by Z. Ko?an) of a set of 24 of these conditions. In the present paper we remove these gaps by giving an example of a triangular map in the square with the following properties:

(1)

all fibre maps are nondecreasing,

(2)

all recurrent points of the map are uniformly recurrent, and

(3)

the restriction of the map to the set of recurrent points has an uncountable scrambled set (and so is Li-Yorke chaotic).

The example is obtained by taking an appropriate Floyd-Auslander minimal system and then taking its appropriate continuous extension to a triangular map of the square.     

An additive-multiplicative rates model for multivariate recurrent events with event categories missing at random

Multivariate recurrent event data arises when study subjects may experience more than one type of recurrent events. In some situations, however, although event times are always observed, event categories may be partially missing. In this paper, an additive-multiplicative rates model is proposed for the analysis of multivariate recurrent event data when event categories are missing at random. A weighted estimating equations approach is developed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. In addition, a model-checking technique is presented to assess the adequacy of the model. Simulation studies are conducted to evaluate the finite sample behavior of the proposed estimators, and an application to a platelet transfusion reaction study is provided.     

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...     

The proportional hazards model for multiple type recurrent gap times

Recurrent events data and gap times between recurrent events are frequently encountered in many clinical and observational studies, and often more than one type of recurrent events is of interest. In this paper, we consider a proportional hazards model for multiple type recurrent gap times data to assess the effect of covariates on the censored event processes of interest. An estimating equation approach is used to obtain the estimators of regression coefficients and baseline cumulative hazard functions. We examine asymptotic properties of the proposed estimators. Finite sample properties of these estimators are demonstrated by simulations.     

The Additive Hazards Model for Multiple Type Recurrent Gap Times

In many biomedical and engineering studies, recurrent event data and gap times between successive events are common and often more than one type of recurrent events is of interest. It is well known that the proportional hazards model may not be appropriate for fitting survival times in some settings. In the paper, we consider an additive hazards model for multiple type recurrent gap times data to assess the effect of covariates. For inferences about regression coefficients and baseline cumulative hazard functions, an estimating equation approach is developed. Furthermore, we establish asymptotic properties of the proposed estimators.     

A Performance evaluation of neural network models in traffic volume forecasting

Forecasting traffic volume is an important task in controlling urban highways, guiding drivers' routes, and providing real-time transportation information. Previous research on traffic volume forecasting has concentrated on a single forecasting model and has reported positive results, which has been frequently better than those of other models. In addition, many previous researchers have claimed that neural network models are better than linear statistical models in terms of prediction accuracy. However, the forecasting power of a single model is limited to the typical cases to which the model fits best. In other words, even though many research efforts have claimed the general superiority of a single model over others in predicting future events, we believe it depends on the data characteristics used, the composition of the training data, the model architecture, and the algorithm itself.In this paper, we have studied the relationship in forecasting traffic volume between data characteristics and the forecasting accuracy of different models, particularly neural network models. To compare and test the forecasting accuracy of the models, three different data sets of traffic volume were collected from interstate highways, intercity highways, and urban intersections. The data sets show very different characteristics in terms of volatility, period, and fluctuations as measured by the Hurst exponent, the correlation dimension. The data sets were tested using a back-propagation network model, a FIR model, and a time-delayed recurrent model.The test results show that the time-delayed recurrent model outperforms other models in forecasting very randomly moving data described by a low Hurst exponent. In contrast, the FIR model shows better forecasting accuracy than the time-delayed recurrent network for relatively regular periodic data described by a high Hurst exponent. The interpretation of these results shows that the feedback mechanism of the previous error, through the temporal learning technique in the time-delayed recurrent network, naturally absorbs the dynamic change of any underlying nonlinear movement. The FIR and back-propagation model, which have claimed a nonlinear learning mechanism, may not be very good in handling randomly fluctuating events.     

A semiparametric additive rates model for clustered recurrent event data

Recurrent event data often arises in biomedical studies, and individuals within a cluster might not be independent. We propose a semiparametric additive rates model for clustered recurrent event data, wherein the covariates are assumed to add to the unspecified baseline rate. For the inference on the model parameters, estimating equation approaches are developed, and both large and finite sample properties of the proposed estimators are established.     

A class of transformation rate models for recurrent event data

Recurrent event data frequently occur in longitudinal studies, and it is often of interest to estimate the effects of covariates on the recurrent event rate. This paper considers a class of semiparametric transformation rate models for recurrent event data, which uses an additive Aalen model as its covariate dependent baseline. The new models are flexible in that they allow for both additive and multiplicative covariate effects, and some covariate effects are allowed to be nonparametric and time-varying. An estimating procedure is proposed for parameter estimation, and the resulting estimators are shown to be consistent and asymptotically normal. Simulation studies and a real data analysis demonstrate that the proposed method performs well and is appropriate for practical use.     

Reoptimization Gaps versus Model Errors in Online-Dispatching of Service Units for ADAC

Under high load, the automated dispatching of service vehicles for the German Automobile As- sociation (ADAC) must reoptimize a dispatch for 100–150 vehicles and 400 requests in about ten seconds to near optimality. In the presence of service contractors, this can be achieved by the column generation algorithm ZIBDIP. In metropolitan areas, however, service contractors cannot be dispatched automatically because they may decline. The problem: a model without contractors yields larger optimality gaps within ten seconds. One way-out are simplified reoptimization mod- els. These compute a short-term dispatch containing only some of the requests: unknown future requests will influence future service anyway. The simpler the models the better the gaps, but also the larger the model error. What is more significant: reoptimization gap or reoptimization model error? We answer this question in simulations on real-world ADAC data: only the new model ZIBDIP_dummy can keep up with ZIBDIP.