Assessing convergence diagnostic tests for Bayesian Cox regression

The Markov chain Monte Carlo (MCMC) method generates samples from the posterior distribution and uses these samples to approximate expectations of quantities of interest. For the process, researchers have to decide whether the Markov chain has reached the desired posterior distribution. Using convergence diagnostic tests are very important to decide whether the Markov chain has reached the target distribution. Our interest in this study was to compare the performances of convergence diagnostic tests for all parameters of Bayesian Cox regression model with different number of iterations by using a simulation and a real lung cancer dataset.     

Estimating a sensitive proportion through randomized response procedures based on auxiliary information

Randomized response techniques are widely employed in surveys dealing with sensitive questions to ensure interviewee anonymity and reduce nonrespondents rates and biased responses. Since Warner’s (J Am Stat Assoc 60:63–69, 1965) pioneering work, many ingenious devices have been suggested to increase respondent’s privacy protection and to better estimate the proportion of people, π _A , bearing a sensitive attribute. In spite of the massive use of auxiliary information in the estimation of non-sensitive parameters, very few attempts have been made to improve randomization strategy performance when auxiliary variables are available. Moving from Zaizai’s (Model Assist Stat Appl 1:125–130, 2006) recent work, in this paper we provide a class of estimators for π _A , for a generic randomization scheme, when the mean of a supplementary non-sensitive variable is known. The minimum attainable variance bound of the class is obtained and the best estimator is also identified. We prove that the best estimator acts as a regression-type estimator which is at least as efficient as the corresponding estimator evaluated without allowing for the auxiliary variable. The general results are then applied to Warner and Simmons’ model.     

管理层持股、领导权结构与财务重述

本文以我国2005-2009年间A股市场补充与更正报告中出现"会计差错更正"项目的公司为研究对象,以是否发生财务重述为因变量,采用配对样本分析的Logit回归方法研究了管理层持股、领导权结构与财务重述之间的关系。本研究发现,管理层持股比例与财务重述之间存在显著的U型关系,领导权结构对财务重述没有显著影响,然而管理层持股的利益协同效应(堑壕效应)在两职分离(两职合一)的公司中更容易得到发挥。本研究发展了财务重述、管理层持股以及领导权结构的研究内容,为完善公司治理机制、防范会计错报提供了相应的思路。     

建立最优回归方程的一种新方法

本文提出了一种新的建立最优回归方程的方法,即t统计量法。应用实践表明该方法完全能够建立起最优回归方程。     

罗莎·卢森堡的马克思主义总体观探微

在马克思主义发展史上,罗莎·卢森堡做出了独创性贡献.这主要表现在她运用总体性方法实现了对马克思主义历史辩证法的革新和丰富上.在她看来,马克思学说是个辩证统一的整体性体系,马克思主义辩证法的最本质的核心是总体性历史观.在坚持马克思主义总体观的前提下,卢森堡剖析了马克思主义的一般原理,并影响了西方马克思主义的产生.     

Regression Methods for Combining Multiple Classifiers

As no single classification method outperforms other classification methods under all circumstances, decision-makers may solve a classification problem using several classification methods and examine their performance for classification purposes in the learning set. Based on this performance, better classification methods might be adopted and poor methods might be avoided. However, which single classification method is the best to predict the classification of new observations is still not clear, especially when some methods offer similar classification performance in the learning set. In this article we present various regression and classical methods, which combine several classification methods to predict the classification of new observations. The quality of the combined classifiers is examined on some real data. Nonparametric regression is the best method of combining classifiers.     

深沪两市公司股本结构与净资产收益率及市净率的回归分析

本文以深沪股市全部上市公司为样本 ,经过将一些特殊的上市公司样本剔除 ,对样本股权结构与上市公司净资产收益率及市净率作相关分析 ,探讨并分析我国上市公司股权结构中各比例与公司绩效的相关性     

论行政权力运行绩效的偏离与回归——关于行政权力制约的一种新的解说

行政权力的公共性特征使公众对它抱有期望 ,然而行政权力的人格化却使行政权力的运行绩效背离公众期望 ,出现了偏差。要使这种偏离回归 ,需要从行政权力产生的源头、行政权力体制的结构和行政权力的惩戒等三个方面采取相应措施     

Testing for Serial Independence: Beyond the Portmanteau Approach

Portmanteau tests are typically used to test serial independence even if, by construction, they are generally powerful only in presence of pairwise dependence between lagged variables. In this article, we present a simple statistic defining a new serial independence test, which is able to detect more general forms of dependence. In particular, differently from the Portmanteau tests, the resulting test is powerful also under a dependent process characterized by pairwise independence. A diagram, based on p-values from the proposed test, is introduced to investigate serial dependence. Finally, the effectiveness of the proposal is evaluated in a simulation study and with an application on financial data. Both show that the new test, used in synergy with the existing ones, helps in the identification of the true data-generating process. Supplementary materials for this article are available online.