Robust inference for estimating equations with nonignorably missing data based on SIR algorithm

Nonresponse is a very common phenomenon in survey sampling. Nonignorable nonresponse – that is, a response mechanism that depends on the values of the variable having nonresponse – is the most difficult type of nonresponse to handle. This article develops a robust estimation approach to estimating equations (EEs) by incorporating the modelling of nonignorably missing data, the generalized method of moments (GMM) method and the imputation of EEs via the observed data rather than the imputed missing values when some responses are subject to nonignorably missingness. Based on a particular semiparametric logistic model for nonignorable missing response, this paper proposes the modified EEs to calculate the conditional expectation under nonignorably missing data. We can apply the GMM to infer the parameters. The advantage of our method is that it replaces the non-parametric kernel-smoothing with a parametric sampling importance resampling (SIR) procedure to avoid nonparametric kernel-smoothing problems with high dimensional covariates. The proposed method is shown to be more robust than some current approaches by the simulations.     

Semiparametric regression of clustered current status data

This paper discusses regression analysis of clustered current status data under semiparametric additive hazards models. In particular, we consider the situation when cluster sizes can be informative about correlated failure times from the same cluster. To address the problem, we present estimating equation-based estimation procedures and establish asymptotic properties of the resulting estimates. Finite sample performance of the proposed method is assessed through an extensive simulation study, which indicates the procedure works well. The method is applied to a motivating data set from a lung tumorigenicity study.     

基于SEM-AHP方法的核电厂定量人为因素评价研究

文章基于人为因素分析与分类系统,分析了115起核电厂执照运行事件报告(Licensed Operation Event Report, LOER),并对影响核电厂事件发生的人为因素进行分类,采用结构方程模型方法得出各影响因素的路径系数,两两比较得到层次分析模型的判断矩阵,进而确定各因素的权重。同时对核电厂人为因素的重要性做排序分析,找到核电厂运行过程中的安全管理薄弱环节,从而促进核电厂系统平稳运行。     

感知风险对在线旅游产品购买意愿的影响分析

旅游电子商务发展迅猛,互联网购物平台的虚拟性和远程性强化了消费者感知风险,它也成为影响旅游电子商务发展的重要因素之一.文章聚焦消费者感知风险,运用SPSS 19.0及AMOS 21.0软件,构建以5个维度的感知风险为自变量、购买意愿为因变量、信任为调节变量的结构方程模型,探讨变量间相关关系.结果发现:感知风险对信任的影响差异较大,仅经济风险感知与信任呈显著负相关;感知风险对购买意愿总效应皆呈负相关;信任作为中介变量存在明显的调节作用,有效地降低了感知风险对购买意愿的负向影响.     

Effects of key enabling technologies for seru production on sustainable performance

Demand uncertainty, economic globalization, and environmental deterioration force factories to innovate their manufacturing systems for achieving sustainable performance. Seru production, which is the latest manufacturing mode developed in Japan, attracts broad attention from both academia and practitioners. The overwhelming majority of existing works on seru production focus on economic performance unilaterally, while neglecting the environmental and social performance. This paper investigates the effects of key enabling technologies for seru production on sustainable performance. Firstly, four key enabling technologies for seru production are identified through systematic review, and an evaluation indicator system of sustainable performance in the context of seru production is developed. Then, the hypotheses about the effects of the identified key enabling technologies for seru production on sustainable performance are proposed on the basis of previous research achievements, theoretical analysis, and practical observations. Finally, the hypotheses are tested through structural equation modeling. Except for two hypotheses which are not supported and one which is indirectly supported, all other hypotheses are verified. The research results show that the four key enabling technologies for seru production have different effects on the three dimensions of sustainable performance. The achievements of this work are of significance to improve the comprehensive understanding of seru production, as well as to develop practical methods to implement sustainable operations.     

On pseudo-values for regression analysis in competing risks models

For regression on state and transition probabilities in multi-state models Andersen et al. (Biometrika 90:15–27, 2003) propose a technique based on jackknife pseudo-values. In this article we analyze the pseudo-values suggested for competing risks models and prove some conjectures regarding their asymptotics (Klein and Andersen, Biometrics 61:223–229, 2005). The key is a second order von Mises expansion of the Aalen-Johansen estimator which yields an appropriate representation of the pseudo-values. The method is illustrated with data from a clinical study on total joint replacement. In the application we consider for comparison the estimates obtained with the Fine and Gray approach (J Am Stat Assoc 94:496–509, 1999) and also time-dependent solutions of pseudo-value regression equations.     

Threshold Estimation for Stochastic Processes with Small Noise

Consider a process satisfying a stochastic differential equation with unknown drift parameter, and suppose that discrete observations are given. It is known that a simple least squares estimator (LSE) can be consistent but numerically unstable in the sense of large standard deviations under finite samples when the noise process has jumps. We propose a filter to cut large shocks from data and construct the same LSE from data selected by the filter. The proposed estimator can be asymptotically equivalent to the usual LSE, whose asymptotic distribution strongly depends on the noise process. However, in numerical study, it looked asymptotically normal in an example where filter was chosen suitably, and the noise was a Lévy process. We will try to justify this phenomenon mathematically, under certain restricted assumptions.     

Model comparison of linear and nonlinear Bayesian structural equation models with dichotomous data

In this article, dichotomous variables are used to compare between linear and nonlinear Bayesian structural equation models. Gibbs sampling method is applied for estimation and model comparison. Statistical inferences that involve estimation of parameters and their standard deviations and residuals analysis for testing the selected model are discussed. Hidden continuous normal distribution (censored normal distribution) is used to solve the problem of dichotomous variables. The proposed procedure is illustrated by a simulation data obtained from R program. Analyses are done by using R2WinBUGS package in R-program.     

The effect of different data structures,sample sizes on model fit measures

In this study, our aim was to investigate the changes of different data structures and different sample sizes on the structural equation modeling and the influence of these factors on the model fit measures. Examining the created structural equation modeling under different data structures and sample sizes, the evaluation of model fit measures were performed with a simulation study. As a result of the simulation study, optimization and negative variance estimation problems have been encountered depending on the sample size and changing correlations. It was observed that these problems disappeared either by increasing the sample size or the correlations between the variables in factor. For upcoming studies, the choice of RMSEA and IFI model fit measures can be suggested in all sample sizes and the correlation values for data sets are ensured the multivariate normal distribution assumption.     

关于Feigenbaum型泛函方程的C^1解

利用Schauder不动点定理、自同胚和紧凸子集的相关性质研究Feigenbaum型泛函方程的连续可微解的存在性和唯一性.