Some sampling effects of pairwise correlated observations on likelihood ratio tests for the difference between two means

We study the effects of the inclusion of pairs of correlated observations in a sample on likelihood ratio tests for the difference in two means. In particular, we assess how the inclusion of correlated data pairs (e.g., such as data inadvertently collected from sib-pairs) affects the sample size requirements necessary for the implementation of a Likelihood Ratio (LR) test for the difference between two means. Our results suggest that correlated data pairs beneficially or adversely effect sample size requirements for an LR test to a degree functionally related to the mixture parameters dictating their relative frequencies in the larger sample on which the test will be performed, the strength of the correlation between the observations, and the size of imbalances in the sample with respect to the number of observations in each group. The relevance and implications of the results for genetic and epidemiologic research are discussed.     

农民工培训的逆向选择及破解对策分析

加快农村富余劳动力转移就业的关键就在于加强农民工培训。目前,政府对农民工培训的投入力度正在逐年加大;但用人单位对农民工培训却缺乏积极性,其深层次原因在于农民工培训市场的“逆向选择”问题。破解对策就是农民工通过“信号传递”的方式使企业了解到其个人素质的完全信息,这样企业将针对高素质农民工与低素质农民工制定不同的服务合同。政府在农民工培训过程中要加强政策导向投入、财政支付投入以及技能凭证管理投入。     

我国高等教育的过度与"考研热"现象分析

我国高校毕业生平均就业率从2001年至今逐年下降,"考研热"持续升温,高等过度教育已经出现。从人力资本理论和信号理论的视角切入高等教育过度带来的"考研热"并进行分析,可以看出高等教育过度会造成知识失业,并对经济增长产生负面影响。因此,国家应从宏观层面对高等教育过度进行制度改进和调控,才能实现高等教育资源高效率优化配置。     

大学数学研究性学习的选题与实施

大学数学教学存在一些问题,开展数学研究性学习的很有必要,大学数学研究性学习选题的需贯彻可研究性、可行性、开放性和实践性四个原则。根据《最优化理论》和《概率论与数理统计》两门课程特点,针对学生课堂学习和课后学习提出了两种开展研究性学习的模式,即课堂自主研究模式和课后课题研究模式。     

从社会责任角度谈和谐医患关系的构建

医患关系本质上反映了医疗服务机构的社会责任感问题。医患关系矛盾性的根源是医疗服务机构的逆向选择和道德风险。广受社会关注的企业社会责任问题中的企业和消费者之间的关系在医疗市场上其实就表现为医患关系。强化医陪服务机构的社会责任感，是构建和谐医患关系的根本。     

从"分数"选拔机制向"配套程序"选拔机制转换的司法人才培养制度——日本新司法考试制度及其启示

日本新司法考试制度是从过去靠"分数"选拔机制向通过"配套程序"选拔新的司法人才培养制度转换的一种新举措。它以法科大学院受到充实的教育以及严格的成绩评价和毕业认定为前提、以法科大学院的教育内容为基础而实施考试。它旨在通过司法考试和经过司法培训,确保法科大学院毕业生拥有法律道德素质以及思考力、分析力、辩论等能力。     

NONPARAMETRIC AUTOCOVARIANCE FUNCTION ESTIMATION

Nonparametric estimators of autocovariance functions for non-stationary time series are developed. The estimators are based on straightforward nonparametric mean function estimation ideas and allow use of any linear smoother (e.g. smoothing spline, local polynomial). The paper studies the properties of the estimators, and illustrates their usefulness through application to some meteorological and seismic time series.     

Graphical Techniques for Selecting Explanatory Variables for Time Series Data

Bayesian model building techniques are developed for data with a strong time series structure and possibly exogenous explanatory variables that have strong explanatory and predictive power. The emphasis is on finding whether there are any explanatory variables that might be used for modelling if the data have a strong time series structure that should also be included. We use a time series model that is linear in past observations and that can capture both stochastic and deterministic trend, seasonality and serial correlation. We propose the plotting of absolute predictive error against predictive standard deviation. A series of such plots is utilized to determine which of several nested and non-nested models is optimal in terms of minimizing the dispersion of the predictive distribution and restricting predictive outliers. We apply the techniques to modelling monthly counts of fatal road crashes in Australia where economic, consumption and weather variables are available and we find that three such variables should be included in addition to the time series filter. The approach leads to graphical techniques to determine strengths of relationships between the dependent variable and covariates and to detect model inadequacy as well as determining useful numerical summaries.     

非对称信息下证券公司道德风险定价模型的研究

由于证券市场上非对称信息状态的存在,信息差别给证券投资者保护制度带来了不容忽视的道德风险结果,本文通过建立证券公司道德风险定价模型,计算出了证券公司向证券投资者保护基金机构缴纳保护基金的费率基准。据此,在保护证券投资者利益的基础上,证券投资者保护基金机构可以向证券公司按照给出的费率基准收取保护基金,从而减少证券公司道德风险的发生。     

Bayesian semiparametric inference for the accelerated failure-time model

Bayesian semiparametric inference is considered for a loglinear model. This model consists of a parametric component for the regression coefficients and a nonparametric component for the unknown error distribution. Bayesian analysis is studied for the case of a parametric prior on the regression coefficients and a mixture-of-Dirichlet-processes prior on the unknown error distribution. A Markov-chain Monte Carlo (MCMC) method is developed to compute the features of the posterior distribution. A model selection method for obtaining a more parsimonious set of predictors is studied. The method adds indicator variables to the regression equation. The set of indicator variables represents all the possible subsets to be considered. A MCMC method is developed to search stochastically for the best subset. These procedures are applied to two examples, one with censored data.