Regression Analysis of Left-truncated and Case I Interval-censored Data with the Additive Hazards Model

In recent years the analysis of interval-censored failure time data has attracted a great deal of attention and such data arise in many fields including demographical studies, economic and financial studies, epidemiological studies, social sciences, and tumorigenicity experiments. This is especially the case in medical studies such as clinical trials. In this article, we discuss regression analysis of one type of such data, Case I interval-censored data, in the presence of left-truncation. For the problem, the additive hazards model is employed and the maximum likelihood method is applied for estimations of unknown parameters. In particular, we adopt the sieve estimation approach that approximates the baseline cumulative hazard function by linear functions. The resulting estimates of regression parameters are shown to be consistent and efficient and have an asymptotic normal distribution. An illustrative example is provided.     

Kernel Density-Based Linear Regression Estimate

For linear regression models with non normally distributed errors, the least squares estimate (LSE) will lose some efficiency compared to the maximum likelihood estimate (MLE). In this article, we propose a kernel density-based regression estimate (KDRE) that is adaptive to the unknown error distribution. The key idea is to approximate the likelihood function by using a nonparametric kernel density estimate of the error density based on some initial parameter estimate. The proposed estimate is shown to be asymptotically as efficient as the oracle MLE which assumes the error density were known. In addition, we propose an EM type algorithm to maximize the estimated likelihood function and show that the KDRE can be considered as an iterated weighted least squares estimate, which provides us some insights on the adaptiveness of KDRE to the unknown error distribution. Our Monte Carlo simulation studies show that, while comparable to the traditional LSE for normal errors, the proposed estimation procedure can have substantial efficiency gain for non normal errors. Moreover, the efficiency gain can be achieved even for a small sample size.     

Inference for a Linear Functional of Cumulative Hazard Function Via Empirical Likelihood

This article discusses sampling plans, that is, the allocation of sampling units, for computing tolerance limits in a balanced one--way random-effects model. The expected width of the tolerance interval is derived and used as the basis for comparing different sampling plans. A well-known cost function and examples are used to facilitate the discussion.     

Likelihood Ratio Order of m-Spacings in Multiple-Outlier Models

This article deals with the multiple-outlier exponential model. The likelihood ratio order between m-spacings of the combined sample is developed, some results extend the conclusions on simple spacings in Wen et al. (2007 Wen , S. , Lu , Q. , Hu , T. ( 2007 ). Likelihood ratio order of spacings of heterogeneous exponential random variables . J. Multivariate Anal. 98 : 743 – 756 .[Crossref], [Web of Science ®] , [Google Scholar]).     

中国制造业集聚：度量与显著性检验 ——基于集聚测量新方法

 根据集聚分布的自身特点,本文提出了可以进行显著性检验的普适性产业集聚指数,并对我国28制造业1994年的860个样本和2004年800个样本进行测量和检验,我们的计算结果表明,中国制造业的集聚强度总体上呈上升趋势,同时具有集聚和扩散双重现象。     

积极进取勇于创新 ——邓小平理论"三进"的教学实践

邓小平理论"进教材、进课堂、进头脑"是高校政治理论课的重要使命.目前,邓小平理论"进教材"、"进课堂"均已做到,但"进头脑"仍是政治理论课教师面临的重要课题.我院在邓小平理论"三进"教学实践中积累了一些经验、取得了一些成绩.     

论希望的逻辑结构

某人有某种希望意指他希望某个命题为真,因而希望是关于命题的模态.希望逻辑是研究希望模态命题之间的逻辑关系.合理的希望是演绎封闭的、一致的、自我肯定的,等等,这些性质可以构成希望逻辑的公理.希望的一个重要的性质是,人们希望不一定为真,但是人们希望所希望的为真;它是希望逻辑的特有公理,可称为“希望公理”.借助于可能世界语义学通过选择不同的公理,可得到一些完全且可靠的希望逻辑系统.     

异域犹将正朔遵——南洋竹枝词的阐释方式与文化建构

作为一种文化现象,竹枝词这一自唐代刘禹锡借鉴巴楚民歌而形成的诗体在南洋以异质文化为摹写对象,通过以诗证史产生全面而深刻的独特认识价值。与传统竹枝词相比较而言,南洋竹枝词所涉及社会生活的广度、深度方面与中国本土有着不同的文学体验,其创作的触角延伸到社会生活的各个角落而具有独特的社会史意义。在艺术上亦显见开拓与创新。