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.     

基于众数回归的变系数部分线性工具变量模型的稳健估计

基于众数回归,利用工具变量研究含有内生变量的变系数部分线性模型的稳健估计.首先,引入工具变量对内生协变量进行分解,从而得到内生协变量的一致估计;其次,运用B样条基函数近似模型中的非参数部分,将模型简化;进一步,基于众数回归的思想,结合EM算法得到参数和非参数函数的估计.在一定条件下,证明估计量的大样本性质;最后,利用模拟实验和真实实例验证所提方法的有效性.     

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.     

缺失数据下EV模型的调整最小二乘估计

该文考虑协变量缺失时的多元线性EV模型参数的估计, 其中协变量的缺失机制是Rubin(1976)提出的随机缺失(MAR). 利用加权调整最小二乘方法给出参数估计, 证明了估计的相合性和渐近正态性. 数值模拟结果表明所给的估计性态良好.     

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.     

成组复发事件下的一般半参数加性乘积比率回归模型

本文在成组复发事件下研究了一个一般半参数的加性乘积比率模型,利用估计方程的理论,给出了该模型中未知参数和基本比率函数的估计,同时利用现代经验过程理论证明了所得估计的相合性和渐近正态性.     

含测量误差的部分线性模型的发散参数估计(英文)

本文考虑了部分线性模型中,线性部分协变量含有测量误差,并且线性部分的参数随着样本量的增大而发散的估计问题.我们考虑了用可观测的替代变量来替代不可观察到的真实变量,这种替代变量的期望与真实变量存在线性关系.我们提出了估计方法,并研究了估计量的相合性与渐进正态性.此外,我们研究了发散参数的发散速度.我们通过模拟来说明该估计的实际效果.     

删失数据下的半参数变系数部分线性回归模型

为了分析删失数据,该文考虑变系数部分线性模型,此模型允许协变量对响应变量存在非线性影响.响应变量与协变量之间关系的统计模型通过线性结构来拟合是非常重要而且有益.对于删失数据,常用的统计方法不能直接应用于此模型.该文首先提出一类数据变换用以建立无偏条件期望.然后利用profile最小二乘方法,给出了模型中参数分量和非参数分量的profile最小二乘估计,并建立了这些估计的渐近正态性.最后通过数值例子来说明该文所提出的方法的有效性.     

内生性协变量下广义变系数模型的工具变量估计

在模型的部分协变量为内生性协变量的情况下,考虑广义变系数模型的一类估计问题.通过结合基函数逼近和一些辅助变量信息,提出了一个基于工具变量的估计过程.并得到了估计的相合性和收敛速度等渐近性质.所提出的估计方法可以有效地消除协变量的内生性对估计精度的影响,并且具有较好的有限样本性质.     

协变量区间删失时线性模型的参数估计

本文研究了当协变量为区间数据时的线性模型，通过构造区间数据变量的条件均值，得到了回归参数的估计，当协变量的分布已知时，证明了估计的无偏性与强相合性．时协变量的分布未知的情形也作了讨论．文中还作了若干模拟计算，从模拟的结果不难发现，利用本文提出的方法所获得的估计简便且具有较高的精度．     

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.     

Additive–multiplicative hazards model with current status data

The additive–multiplicative hazards (AMH) regression model specifies an additive and multiplicative form on the hazard function for the counting process associated with a multidimensional covariate process, which contains the Cox proportional hazards model and the additive hazards model as its special cases. In this paper, we study the AMH model with current status data, where the cumulative hazard hazard function is assumed to be nonparametric and is estimated using B-splines with monotonicity constraint on the functional, while a simultaneous sieve maximum likelihood estimation is proposed to estimate regression parameters. The proposed estimator for the parameter vector is shown to be asymptotically normal and semiparametric efficient. The B-splines estimator of the functional of the cumulative hazard function is shown to achieve the optimal nonparametric rate of convergence. A simulation study is conducted to examine the finite sample performance of the proposed estimators and algorithm, and a real data example is presented for illustration.     

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...     

带有随机产出和广告投入的Newsboy模型

首次将随机产出和广告投入同时引入到Newsboy模型中,分别在乘积形式随机产出和加和形式随机产出情况下,建立带有广告费用的最优决策模型,通过分析得出如下结论:在乘积随机产出情况下,随着广告费用的不断增加,最优计划生产量在快速增加;而在加和形式随机产出情况下,随着广告费用不断增加时,最优计划生产量也在不断增加,但其增加的速率较小。最后,指出乘积形式的随机产出适用于刚上市的新产品,而加和形式的随机产出适用于品牌产品。     

含有治愈部分的可加危险模型的估计

最近可加危险(AH)模型被广泛地应用于生存分析数据,模型的协变量可以假设为时间独立或时间相关的.基于混合治愈模型,有界累计危险治愈模型和"不正确"的比例危险模型.本文将上述的可乘危险模型延伸到可加的危险模型,这里的模型可以允许含治愈部分的生存数据的存在."不正确"的AH模型的识别和参数估计也将在本文给出讨论.     

Modeling and Anomalous Cluster Detection for Point Processes Using Process Convolutions

We present a model using process convolutions, which describes spatial and temporal variations of the intensity of events that occur at random geographical locations. An inhomogeneous Poisson process is used to model the intensity over a spatial region with multiplicative spatial and temporal covariate effects. Temporal variation in the structure of the intensity is obtained by employing a time-varying process for the convolution. Use of a compactly supported kernel in the convolution improves the computational efficiency. Additionally, anomalous cluster detection in the event rates is developed based on exceedance probabilities. The methods are demonstrated on data of major crimes in Cincinnati during 2006. Supplementary materials for this article are available online.     

竞争风险数据和协变量相依权重下可加可乘的子分布风险率模型

本文在竞争风险数据下提出一种灵活的含变系数的可加可乘的子分布风险率模型.通过对删失时间的风险函数建立Cox比例风险模型,得到调整后的与协变量相依的权重,在新权重下建立估计方程来估计模型参数,并获得了估计的大样本性质,同时提出了模型中协变量的时变效应的检验方法.通过数值模拟验证了所提方法的有限样本性质,结果表明所提方法可以大大降低估计偏差.最后,分析了一组淋巴滤泡细胞的竞争风险数据集来展示所提方法的实际应用效果.     

A general additive-multiplicative rates model for recurrent event data

In this article, we propose a general additive-multiplicative rates model for recurrent event data. The proposed model includes the additive rates and multiplicative rates models as special cases. For the inference on the model parameters, estimating equation approaches are developed, and asymptotic properties of the proposed estimators are established through modern empirical process theory. In addition, an illustration with multiple-infection data from a clinic study on chronic granulomatous disease is pr...     

Strong convergence of a stochastic Rosenbrock-type scheme for the finite element discretization of semilinear SPDEs driven by multiplicative and additive noise

This paper aims to investigate the numerical approximation of a general second order parabolic stochastic partial differential equation(SPDE) driven by multiplicative and additive noise. Our main interest is on such SPDEs where the nonlinear part is stronger than the linear part, usually called stochastic dominated transport equations. Most standard numerical schemes lose their good stability properties on such equations, including the current linear implicit Euler method. We discretize the SPDE in space by the finite element method and propose a novel scheme called stochastic Rosenbrock-type scheme for temporal discretization. Our scheme is based on the local linearization of the semi-discrete problem obtained after space discretization and is more appropriate for such equations. We provide a strong convergence of the new fully discrete scheme toward the exact solution for multiplicative and additive noise and obtain optimal rates of convergence. Numerical experiments to sustain our theoretical results are provided.     

Semiparametric additive intensity model with frailty for recurrent events

The seminal Cox’s proportional intensity model with multiplicative frailty is a popular approach to analyzing the frequently encountered recurrent event data in scientific studies. In the case of violating the proportional intensity assumption, the additive intensity model is a useful alternative. Both the additive and proportional intensity models provide two principal frameworks for studying the association between the risk factors and the disease recurrences. However, methodology development on the additive intensity model with frailty is lacking, although would be valuable. In this paper, we propose an additive intensity model with additive frailty to formulate the effects of possibly time-dependent covariates on recurrent events as well as to evaluate the intra-class dependence within recurrent events which is captured by the frailty variable. The asymptotic properties for both the regression parameters and the association parameters in frailty distribution are established. Furthermore, we also investigate the large-sample properties of the estimator for the cumulative baseline intensity function.