Estimation of the joint survival function for successive duration times under double-truncation and right-censoring

ABSTRACT

In incident cohort studies, survival data often include subjects who have had an initiate event at recruitment and may potentially experience two successive events (first and second) during the follow-up period. When disease registries or surveillance systems collect data based on incidence occurring within a specific calendar time interval, the initial event is usually subject to double truncation. Furthermore, since the second duration process is observable only if the first event has occurred, double truncation and dependent censoring arise. In this article, under the two sampling biases with an unspecified distribution of truncation variables, we propose a nonparametric estimator of the joint survival function of two successive duration times using the inverse-probability-weighted (IPW) approach. The consistency of the proposed estimator is established. Based on the estimated marginal survival functions, we also propose a two-stage estimation procedure for estimating the parameters of copula model. The bootstrap method is used to construct confidence interval. Numerical studies demonstrate that the proposed estimation approaches perform well with moderate sample sizes.     

Bayesian estimation and influence diagnostics of generalized partially linear mixed-effects models for longitudinal data

This paper develops a Bayesian approach to obtain the joint estimates of unknown parameters, nonparametric functions and random effects in generalized partially linear mixed models (GPLMMs), and presents three case deletion influence measures to identify influential observations based on the φ-divergence, Cook's posterior mean distance and Cook's posterior mode distance of parameters. Fisher's iterative scoring algorithm is developed to evaluate the posterior modes of parameters in GPLMMs. The first-order approximation to Cook's posterior mode distance is presented. The computationally feasible formulae for the φ-divergence diagnostic and Cook's posterior mean distance are given. Several simulation studies and an example are presented to illustrate our proposed methodologies.     

Goodness-of-fit tests of generalized linear mixed models for repeated ordinal responses

Categorical longitudinal data are frequently applied in a variety of fields, and are commonly fitted by generalized linear mixed models (GLMMs) and generalized estimating equations models. The cumulative logit is one of the useful link functions to deal with the problem involving repeated ordinal responses. To check the adequacy of the GLMMs with cumulative logit link function, two goodness-of-fit tests constructed by the unweighted sum of squared model residuals using numerical integration and bootstrap resampling technique are proposed. The empirical type I error rates and powers of the proposed tests are examined by simulation studies. The ordinal longitudinal studies are utilized to illustrate the application of the two proposed tests.     

An F-type test for detecting departure from monotonicity in a functional linear model

When studying associations between a functional covariate and scalar response using a functional linear model (FLM), scientific knowledge may indicate possible monotonicity of the unknown parameter curve. In this context, we propose an F-type test of monotonicity, based on a full versus reduced nested model structure, where the reduced model with monotonically constrained parameter curve is nested within an unconstrained FLM. For estimation under the unconstrained FLM, we consider two approaches: penalised least-squares and linear mixed model effects estimation. We use a smooth then monotonise approach to estimate the reduced model, within the null space of monotone parameter curves. A bootstrap procedure is used to simulate the null distribution of the test statistic. We present a simulation study of the power of the proposed test, and illustrate the test using data from a head and neck cancer study.     

“镇改市”过程中的制约因素与改革进路

“镇改市”是我国基层行政体制改革的试验,也是在新型城镇化过程中对基层行政区划改革做出的有益尝试。以构建大中小城市和小城镇协调发展的城镇化空间格局为目标指向,通过激活中小城市的发展“命脉”,改变单纯依靠经济推动乡镇发展的路径依赖,实现“农民城”向新型城镇的跨越,是中国经济发展的新探索。然而,随着改革的推进,缺位的行政规划设计、有限的乡镇管理能力、被异化的政绩观念、社会组织发育不成熟以及市民文化普及不到位等,对“镇改市”的推进造成一定程度的制约,成为“镇改市”过程中难以回避的现实问题。问题的化解离不开政策的引导、技术的支持和法治的保障,这三个维度相互联系、缺一不可,共同为“镇改市”改革的顺利实施“保驾护航”。     

城投公司债券信用利差影响因素与对策研究

为防范政府债务风险,减少城投债信用违约事件的发生,采用多元线性回归模型,选取2014～2016年新发城投债中7年期公司债为样本,将政府财政及债券等个体因素相关指标作为自变量,以宏观因素指标作为控制变量,分析中国不同行政级别城投债信用利差的影响因素。研究认为,债券个体因素对县、市和省级城投债信用利差均有显著影响,宏观因素的影响随城投公司所属行政级别升高而升高;地方政府财政因素对各行政级别的影响方向相同,由于城投公司资质存在差异,政府财政因素对低行政级别信用利差的影响显著;不同行政级别下,各类因素对信用利差的影响略有不同,城投公司债券发行信用利差与评级情况和发行规模都呈负向关系,系数绝对值随行政级别的升高而降低,企业自身财务因素影响并不显著。地方政府应大力发展经济使城投公司融资成本降低,投资者在投资前利用多种指标估算债券发行信用利差,城投公司也要注意合理设计发债规模,使自身融资成本达到最低,监管机构加强对低行政级别城投公司债券发行的监管和宏观经济方面的政策引导,以此促进解决城投债问题和推动城投债公司合理发行债券。     

大型科研项目的风险管理流程与风险决策

针对大型科研项目风险管理的特点,设计了其风险管理流程;根据风险决策原理,结合实例探讨了灰局势决策法在大型科研项目中的应用,所获得的决策结果是进行风险处理的基础和重要依据,对风险管理实践有一定的指导意义。     

在内外因的交互作用中把握德育过程

德育是一个综合化、系统化的内外因交互作用的过程,它既包括个人的内化过程,也包括诸如家庭、学校、社会的外塑过程。只有认真把握内化和外塑这两个过程的特点和规律,不断创新德育工作方法,才能使德育工作收到实效。本文着重论述了德育过程中个人"内化"过程的主动性、自主性、独立性、体验性,以及作为德育"外塑"过程的家庭德育过程、学校德育过程、社会德育过程各自的地位、特点、存在的问题以及解决问题的方法,以期对做好德育工作起到积极作用。     

协商民主与政治参与——一种重要的扩大公民有序政治参与的民主形式

协商民主是一种重要的扩大有序政治参与的民主形式。其主要体现为：协商民主有利于整合政治过程中的利益冲突;有利完善政治系统的结构与功能;有利于形成科学和民主的公共决策;有利于促进参与型政治文化的形成。