The spokane heart study: weibull regression and coronary artery disease

Coronary artery calcium is a marker of coronary artery disease and measures the progression of atherosclerosis. It is measured by electron beam computed tomography, and the measured amount of coronary artery calcium is highly skewed to the right and left censored. The distribution of coronary artery calcium appears to be Weibull. We propose a Weibull regression model and we analyze the data using these techniques. Our analysis is based on data from the Spokane Heart Study, which is a cohort of about a thousand subjects that are assessed every two years for coronary artery calcium and risk factors of coronary artery disease. The major focus of the heart study is to determine the natural history of atherosclerosis in its early phase, and we analyze the data as a cross-sectional study with 859 subjects. We would also like to highlight the use of Weibull regression techniques in situations like this, where we have extreme right skewed data. Our main emphasis will be on examining the effect of the traditional risk factors of age, gender, lipid profile (cholesterol and HDL), patient history of lipid abnormality, hypertension, and smoking, and other family history risks on coronary artery calcium. We found that the most important factors influencing the disease were age, sex, and patient history of smoking and lipid abnormality.     

Quasi-Likelihood Approach to Bioavailability and Bioequivalence Analysis

The outcomes AUCT (area-under-curve from time zero to time t) of n individuals randomized to one of two groups TR or RT, where the group name denotes the order in which the subjects receive a test formulation (T) or a reference formulation (R), are used to assess average bioequivalence for the two formulations. The classical method is the mixed model, for example, proc mixed or proc glm with random statement in SAS can be used to analyze this type of data. This is equivalent to the marginal likelihood approach in a normal–normal model. There are some limitations for this approach. It is not appropriate if the random effect is not normally distributed. In this article, we introduce a hierarchical quasi-likelihood approach. Instead of assuming the random effect is normal, we make assumptions only about the mean and the variance function of the random effect. Our method is flexible to model the random effect. Since we can estimate the random effect for each individual, we can check the adequacy of the distribution assumption about the random effect. This method can also be used to handle high-dimensional crossover data. Simulation studies are conducted to check the finite sample performance of the method under various conditions and two real data examples are used for illustration.     

Focused Information Criterion and Model Averaging in Quantile Regression

In this article, we study model selection and model averaging in quantile regression. Under general conditions, we develop a focused information criterion and a frequentist model average estimator for the parameters in quantile regression model, and examine their theoretical properties. The new procedures provide a robust alternative to the least squares method or likelihood method, and a major advantage of the proposed procedures is that when the variance of random error is infinite, the proposed procedure works beautifully while the least squares method breaks down. A simulation study and a real data example are presented to show that the proposed method performs well with a finite sample and is easy to use in practice.     

An Exponential-Gamma Convolution Model for Background Correction of Illumina BeadArray Data

Illumina BeadArrays are becoming an increasingly popular Microarray platform due to its high data quality and relatively low cost. One distinct feature of Illumina BeadArrays is that each array has thousands of negative control bead types containing oligonucleotide sequences that are not specific to any target genes in the genome. This design provides a way of directly estimating the distribution of the background noise. In the literature of background correction for BeadArray data, the information from negative control beads is either ignored, used in a naive way that can lead to a loss in efficiency, or the noise is assumed to be normally distributed. However, we show with real data that the noise can be skewed. In this study, we propose an exponential-gamma convolution model for background correction of Illumina BeadArray data. Using both simulated and real data examples, we show that the proposed method can improve the signal estimation and detection of differentially expressed genes when the signal to noise ratio is large and the noise has a skewed distribution.     

为什么农民无法从“公司+农户”中受益

为了探究以“公司+农户”为主体的农业产业化经营蓬勃发展与农民增收乏力之间的矛盾,文章以江西省部分农业龙头企业和农户的调查资料为基础,从家庭经营规模、利益联结机制和龙头企业实力三个方面分析了导致目前农民无法受益的原因:以妇女和老人为农业生产主体的农村“空心化”现状使得参与农业产业化经营的农户家庭经营规模偏小;农民和公司的主体地位不对称,农民难以参与农产品加工、销售环节的利润分配,农户和公司之间是松散型利益联结机制;农业龙头企业普遍是小企业,生存的压力已经令其自顾不暇,即使有心也无力让为数众多的农户参与到有限利润的分配中来.     

The Changing Mode of Legal Regulation of Labor Relations in China

At present, Chinese labor relations operate under a unitary model of legal regulation in which labor law is applied to workers “as a whole, with everyone treated equally.” This causes a number of problems. In reality, due to constantly changing modes of employment and the flexibility of workers’ “affiliation,” forms of employment are highly diverse. In addition to regular employees, there are also “quasi-employees” (employee-like persons) whose affiliation is quite weak, and other special employees. This necessitates the updating of the legislative thinking behind labor laws. While holding fast to the assumption that workers are a vulnerable group, we should review the differences between different types of employee and draw up the corresponding regulations on the basis of specific situations. Where legislative technique is concerned, we should do away with the traditional one-size-fits-all legislative model in favor of a dedicated model that targets particular types of subject and particular circumstances. We should abandon the traditional old-fashioned model in which “all labor law provisions apply” or “no labor law provisions apply” and categorize employees in such a way as to provide special rules for quasi-employees, special employees and employees of special employers. The legal regulation of labor relations should be based on categorization and differential treatment.     

“主位”视角下光棍社会地位的再研究——基于江汉平原的经验调查

光棍在传统社会中成为一种依附性的力量,嵌入到村庄结构中。光棍是弱势的文化指涉始终是乡土社会实践的“非意外结果”。由此,学者通过“婚姻挤压”和“婚姻排斥”导演出光棍的“被迫性”问题。但是,随着差序格局的理性化和传统村落社区的解体,光棍的“弱势”意蕴逐渐得以“祛魅”。基于经验调查,本文从“主位”视角出发展开对光棍社会地位的再研究,从经济条件的优厚者、婚姻的高价者、家庭结构中的当家者、人情的积极互动者、关系和资源的拥有者五个方面来呈现出光棍较高的社会地位,打破光棍弱势之迷,光棍只是作为一种生活方式的选择而已,这样就将光棍研究进一步推向“主位选择”的纵深层面。     

东莞市近30年不透水面时空演变研究

城市不透水面作为城市化生态影响评价的重要因素受到社会的广泛关注,掌握不透水面的空间分布和动态变化信息,对生态环境保护和"海绵城市"建设具有重要的意义。本研究以东莞市为研究区,选取1988年、1994年、2005年和2015年四期TM/OLI遥感影像作为数据源,采用不透水面指数法提取研究区的不透水面信息,并结合景观格局指数分析探究东莞市近30年不透水面分布的时空演变规律。研究结果表明:ENDISI指数法可以有效消除山体阴影噪音信息引起的不透水面信息误判,具有较高的提取精度,能较好地反映城市扩张趋势,可为城市内涝治理和"海绵城市"建设提供数据基础;东莞市近30年来不透水面景观格局变化极其显著,面积增长近25.4倍,呈"主-副多中心、轴线延伸式"快速发展,不透水面优势度大幅提升,其连通性和聚集度在1994年之后逐渐加强。     

The likelihood ratio test for high-dimensional linear regression model

The paper considers a significance test of regression variables in the high-dimensional linear regression model when the dimension of the regression variables p, together with the sample size n, tends to infinity. Under two sightly different cases, we proved that the likelihood ratio test statistic will converge in distribution to a Gaussian random variable, and the explicit expressions of the asymptotical mean and covariance are also obtained. The simulations demonstrate that our high-dimensional likelihood ratio test method outperforms those using the traditional methods in analyzing high-dimensional data.