Birthdate and mortality: an evaluation of the death-dip/death-rise

The effect on mortality of socially significant events is examined using birth date as the criterion date for a large sample from official state mortality records for Ohio. The sample consisted of all deaths from natural causes for 1979-1981. "The findings show that there is a greater tendency for persons to die within thirty days after the date of birth than before. More specifically, there was a statistically significant pattern of increased mortality for those who were never married and for ill-defined causes of death. A theoretical foundation and a brief interpretation of these findings is offered."     

Minimization of maximum lateness under linear deterioration

This paper considers a single-machine scheduling problem to minimize the maximum lateness. The processing time of each job is a linear function of the time when the job starts processing. This problem is known to be -hard in the literature. In this paper, we design a branch-and-bound algorithm for deriving exact solutions by incorporating several properties concerning dominance relations and lower bounds. These properties produce synergic effects in accelerating the solution finding process such that the algorithm can solve problems of 100 jobs within 1 min on average. To compose approximate solutions, we revise a heuristic algorithm available in the literature and propose several hybrid variants. Numerical results evince that the proposed approaches are very effective in successfully reporting optimal solutions for most of the test cases.     

对纵贯数据统计分析的认识

在介绍了纵贯数据的设计思想及优缺点基础上,从统计方法的角度讨论了纵贯数据在社会科学中所能发挥的作用。纵贯数据的优点在于其可以帮助我们进行对总体异质性的识别、对因果机制干预的研究、对因果效应的研究和对状态变换的研究。以一些基于纵贯数据的研究设计为实例,文章阐述了在研究中假设与数据紧密衔接的重要性,以及利用统计方法分析纵贯数据时需要考虑的要点。但由于存在着由人类和人类行为内在变异性导致的这一无法避免的根本性缺陷,纵贯数据并不能解决所有问题。因而在纵贯数据的辅助下,研究者需要对社会现象有更深入的理解,将其进行更合理的概念化,并加以更精准的数据分析。     

人口预测的随机方法:基于Leslie矩阵和ARMA模型

本文探讨了人口预测的一种随机方法。文章回顾了经典的Leslie矩阵并结合中国的人口统计数据,用时间序列的ARMA模型对未来的生育率、死亡率进行估计,并由此构造Les-lie矩阵,经时间序列的数据中心化,根据自相关函数、偏自相关函数的截尾性或拖尾性,以及贝叶斯信息准则函数方法对模型定阶,实现对ARMA模型的识别。在中国人口预测方面的应用证明,基于Leslie矩阵和ARMA模型的人口随机预测方法是稳健的,具有很强的适用性。由于统计数据可获得性的局限,对模型做了不少假设和近似。随着人口数据的积累,未来将会在此方面有所改进。     

Estimation and goodness-of-fit for the Cox model with various types of censored data

The currently existing estimation methods and goodness-of-fit tests for the Cox model mainly deal with right censored data, but they do not have direct extension to other complicated types of censored data, such as doubly censored data, interval censored data, partly interval-censored data, bivariate right censored data, etc. In this article, we apply the empirical likelihood approach to the Cox model with complete sample, derive the semiparametric maximum likelihood estimators (SPMLE) for the Cox regression parameter and the baseline distribution function, and establish the asymptotic consistency of the SPMLE. Via the functional plug-in method, these results are extended in a unified approach to doubly censored data, partly interval-censored data, and bivariate data under univariate or bivariate right censoring. For these types of censored data mentioned, the estimation procedures developed here naturally lead to Kolmogorov-Smirnov goodness-of-fit tests for the Cox model. Some simulation results are presented.     

Progressive multi-state models for informatively incomplete longitudinal data

Progressive multi-state models provide a convenient framework for characterizing chronic disease processes where the states represent the degree of damage resulting from the disease. Incomplete data often arise in studies of such processes, and standard methods of analysis can lead to biased parameter estimates when observation of data is response-dependent. This paper describes a joint analysis useful for fitting progressive multi-state models to data arising in longitudinal studies in such settings. Likelihood based methods are described and parameters are shown to be identifiable. An EM algorithm is described for parameter estimation, and variance estimation is carried out using the Louis’ method. Simulation studies demonstrate that the proposed method works well in practice under a variety of settings. An application to data from a smoking prevention study illustrates the utility of the method.     

Modeling longitudinal binomial responses: implications from two dueling paradigms

The generalized estimating equations (GEEs) and generalized linear mixed-effects model (GLMM) are the two most popular paradigms to extend models for cross-sectional data to a longitudinal setting. Although the two approaches yield well-interpreted models for continuous outcomes, it is quite a different story when applied to binomial responses. We discuss major modeling differences between the GEE- and GLMM-derived models by presenting new results regarding the model-driven differences. Our results show that GLMM induces some artifacts in the marginal models at assessment times, making it inappropriate when applied to such responses from real study data. The different interpretations of parameters resulting from the conceptual difference between the two modeling approaches also carry quite significant implications and ramifications with respect to data and power analyses. Although a special case involving a scale difference in parameters between GEE and GLMM has been noted in the literature, its implications in real data analysis has not been thoroughly addressed. Further, this special case has a very limited covariate structure and does not apply to most real studies, especially multi-center clinical trials. The new results presented fill a substantial gap in the literature regarding the model-driven differences between the two dueling paradigms.     

A Cross-Validation Analysis of Neural Network Out-of-Sample Performance in Exchange Rate Forecasting

Econometric methods used in foreign exchange rate forecasting have produced inferior out-of-sample results compared to a random walk model. Applications of neural networks have shown mixed findings. In this paper, we investigate the potentials of neural network models by employing two cross-validation schemes. The effects of different in-sample time periods and sample sizes are examined. Out-of-sample performance evaluated with four criteria across three forecasting horizons shows that neural networks are a more robust forecasting method than the random walk model. Moreover, neural network predictions are quite accurate even when the sample size is relatively small.     

企业节能减排考核与项目环评的协调问题研究

为推进企业节能减排工作的实施,项目的环境影响评价成为企业落实节能减排工作的重要手段,但企业节能减排考核与项目环评在评价内容、重点行业、评价方法和指标体系等方面存在很多不协调之处。本文在分析企业节能减排考核与项目环境影响评价基本框架和内容的基础上,分析了我国企业节能减排考核与项目环境影响评价协调的客观需要,对节能减排考核与项目环境影响评价的有效协调提出了相关对策。