Measures of Association and Visualization of Log Odds Ratio Structure for a Two Way Contingency Table

The odds ratio (OR) is a measure of association used for analysing an I × J contingency table. The total number of ORs to check grows with I and J. Several statistical methods have been developed for summarising them. These methods begin from two different starting points, the I × J contingency table and the two‐way table composed by the ORs. In this paper we focus our attention on the relationship between these methods and point out that, for an exhaustive analysis of association through log ORs, it is necessary to consider all the outcomes of these methods. We also introduce some new methodological and graphical features. In order to illustrate previously used methodologies, we consider a data table of the cross‐classification of the colour of eyes and hair of 5387 children from Scotland. We point out how, through the log OR analysis, it is possible to extract useful information about the association between variables.     

文本解读中的效果历史意识

效果历史意识是哲学解释学的核心概念之一。它是理解和解释得以展开的基础,驱动解释学循环的动力。效果历史意识涵盖文本阅读效果史、生活经验、传统以及读者与文本的对话关系。因此它既是相对固定的整体,又是不断运动、调整、开放的结构,同时蕴含着对解读的历史先在给定性的反思,这是读者必须恪守的“效果历史原则”。效果历史意识的本质是对存在时间的经验,只有真正实现了的时间才能被经验到,从而即便过去,也能收藏在效果历史意识中,在文本解读时重返当下与文本对话,参与文本意义建构。读者的效果历史意识通过与文本的平等交流才能得以自我检视,一部经典只有不断地与读者的效果历史意识对话,才能常新长生。     

区域自主创新实力动态评价——基于改进二次加权法

建立区域自主创新实力评价指标体系,运用改进归一化法和变异系数法对数据进行标准化处理,确定权重,利用改进二次加权法计算2001—2010年中国大陆31个省、市、自治区的区域自主创新实力加权平均值,进行动态评价与分类比较分析。研究表明:2001—2010年期间中国区域自主创新实力从评价值排序看,地域差异明显,东部地区较强,中部地区居中,西部地区较弱。主要原因是东部地区的资金、政策、人才和创新载体的优势,中部、东北地区产业结构和企业创新理念的制约以及西部经济、教育和科研水平相对较低。研究认为:自主创新实力较弱的区域可以通过增加R&D投入、引进创新型人才、优化产业结构、完善创新扶持政策、增强产学研协同创新等手段,提高区域自主创新实力。     

A simulation study on power comparisons for group sequential tests of non-parametric statistics

In this article, a group sequential test (GST) of non-parametric statistics for survival data is briefly reviewed. An asymptotic joint distribution of the test statistics, obtained after each interim analysis, is given to illustrate the applicability of the critical values of the GST procedures. It should be noted that censored observations are generally seen in survival data. Therefore, if one makes power calculations irrespective of censoring, reliable results may not be achieved, due to the lack of information about the censoring structure. A wide simulation study, covering different censoring rates and tied observations, is conducted to make the power comparisons under various scenarios. The simulation results are interpreted and compared with the results obtained by using power analysis and sample size (PASS) software.     

Dimensionality reduction approach to multivariate prediction

The authors consider dimensionality reduction methods used for prediction, such as reduced rank regression, principal component regression and partial least squares. They show how it is possible to obtain intermediate solutions by estimating simultaneously the latent variables for the predictors and for the responses. They obtain a continuum of solutions that goes from reduced rank regression to principal component regression via maximum likelihood and least squares estimation. Different solutions are compared using simulated and real data.     

Robust methods of estimation of regression coefficients 1

Recently, cumulative residual entropy (CRE) has been found to be a new measure of information that parallels Shannon's entropy (see Rao et al. [Cumulative residual entropy: A new measure of information, IEEE Trans. Inform. Theory. 50(6) (2004), pp. 1220–1228] and Asadi and Zohrevand [On the dynamic cumulative residual entropy, J. Stat. Plann. Inference 137 (2007), pp. 1931–1941]). Motivated by this finding, in this paper, we introduce a generalized measure of it, namely cumulative residual Renyi's entropy, and study its properties. We also examine it in relation to some applied problems such as weighted and equilibrium models. Finally, we extend this measure into the bivariate set-up and prove certain characterizing relationships to identify different bivariate lifetime models.     

关于Gauss-Weierstrass算子线性组合的加权逼近

本文研究Gauss-Weierstrass算子的一类线性组合加Jacobi权的一致逼近问题，给出了逼近的正、逆定理和逼近阶的特征刻划。     

Modeling longitudinal count data with dropouts

This paper explores the utility of different approaches for modeling longitudinal count data with dropouts arising from a clinical study for the treatment of actinic keratosis lesions on the face and balding scalp. A feature of these data is that as the disease for subjects on the active arm improves their data show larger dispersion compared with those on the vehicle, exhibiting an over‐dispersion relative to the Poisson distribution. After fitting the marginal (or population averaged) model using the generalized estimating equation (GEE), we note that inferences from such a model might be biased as dropouts are treatment related. Then, we consider using a weighted GEE (WGEE) where each subject's contribution to the analysis is weighted inversely by the subject's probability of dropout. Based on the model findings, we argue that the WGEE might not address the concerns about the impact of dropouts on the efficacy findings when dropouts are treatment related. As an alternative, we consider likelihood‐based inference where random effects are added to the model to allow for heterogeneity across subjects. Finally, we consider a transition model where, unlike the previous approaches that model the log‐link function of the mean response, we model the subject's actual lesion counts. This model is an extension of the Poisson autoregressive model of order 1, where the autoregressive parameter is taken to be a function of treatment as well as other covariates to induce different dispersions and correlations for the two treatment arms. We conclude with a discussion about model selection. Published in 2009 by John Wiley & Sons, Ltd.     

Non-Gaussian Ornstein–Uhlenbeck-based models and some of their uses in financial economics

Non-Gaussian processes of Ornstein–Uhlenbeck (OU) type offer the possibility of capturing important distributional deviations from Gaussianity and for flexible modelling of dependence structures. This paper develops this potential, drawing on and extending powerful results from probability theory for applications in statistical analysis. Their power is illustrated by a sustained application of OU processes within the context of finance and econometrics. We construct continuous time stochastic volatility models for financial assets where the volatility processes are superpositions of positive OU processes, and we study these models in relation to financial data and theory.