单位根的秩检验及其应用研究

 本文系统研究了单位根检验的三种非参数秩检验统计量：RDF检验、得分秩( )检验与逆得分秩( )检验,推导了逆得分秩的分布。然后运用Monte Carlo模拟给出了各种秩检验的临界值,将秩检验与传统检验的优劣进行了比较,并将这些方法应用于我国货币需求函数各变量月度序列的实证检验。Monte Carlo模拟和实证应用的结果表明,秩检验与传统单位根检验方法相结合可以发现序列中的非线性特征,这将有助于对时间序列特征的深入分析。     

Psychological ‘self–other overlap’ and support for slavery reparations

Neurobiological and social psychological studies suggest the existence of non-conscious ‘self–other overlap’ that can lead individuals to identify with other individuals or groups. This paper investigates whether this effect may help to explain support by some Whites for an otherwise unpopular policy on behalf of African Americans-slavery reparations. A representative telephone survey (n = 1200) serves as baseline of comparison for an online reaction time study with a non-representative sample (n = 1341) that measures ‘self–other overlap’ (interchangeably referred to as “implicit closeness” to Blacks). Partial proportional odds ordered logit analyses reveal implicit closeness to Blacks as the single most powerful predictor of support for reparations among White respondents. The magnitude of the absolute effect of implicit closeness exceeds that of traditional predictors such as racial resentment, ideology, and party identification. Methodological and political implications are discussed.     

试论执业医师资格考试的影响因素

我国《执业医师法》已经实施以来,由于我国的政治、经济体制不断变革,国际医学教育形势发生了深刻变化,使《执业医师法》在实施过程中遇到了一些具体问题。在分析执业医师考试影响因素的基础上,与国外执业医师考试的做法进行比较,提出对我国执业医师考试考生报考资格进行修改的建议,有利于加强我国医师队伍的规范化管理,确保医师队伍整体素质的不断优化和提高。     

Semiparametric regression for the mean and rate functions of recurrent events

The counting process with the Cox-type intensity function has been commonly used to analyse recurrent event data. This model essentially assumes that the underlying counting process is a time-transformed Poisson process and that the covariates have multiplicative effects on the mean and rate function of the counting process. Recently, Pepe and Cai, and Lawless and co-workers have proposed semiparametric procedures for making inferences about the mean and rate function of the counting process without the Poisson-type assumption. In this paper, we provide a rigorous justification of such robust procedures through modern empirical process theory. Furthermore, we present an approach to constructing simultaneous confidence bands for the mean function and describe a class of graphical and numerical techniques for checking the adequacy of the fitted mean–rate model. The advantages of the robust procedures are demonstrated through simulation studies. An illustration with multiple-infection data taken from a clinical study on chronic granulomatous disease is also provided.     

Extended and updated tables for the Friedman rank test

The Friedman's test is used for assessing the independence of repeated experiments resulting in ranks, summarized as a table of integer entries ranging from 1 to k, with k columns and N rows. For its practical use, the hypothesis testing can be derived either from published tables with exact values for small k and N, or using an asymptotic analytical approximation valid for large N or large k. The quality of the approximation, measured as the relative difference of the true critical values with respect those arising from the asymptotic approximation is simply not known. The literature review shows cases where the wrong conclusion could have been drawn using it, although it may not be the only cause of opposite decisions. By Monte Carlo simulation we conclude that published tables do not cover a large enough set of (k, N) values to assure adequate accuracy. Our proposal is to systematically extend existing tables for k and N values, so that using the analytical approximation for values outside it will have less than a prescribed relative error. For illustration purposes some of the tables have been included in the paper, but the complete set is presented as a source code valid for Octave/Matlab/Scilab etc., and amenable to be ported to other programming languages.     

The impact of delayed maternity on foetal growth in Spain: An assessment by population attributable fraction

Background

Delayed childbearing is considered a risk factor for maternal–foetal health. As in other higher-income countries, in Spain age at maternity has steadily increased during the last two decades.

Robust estimation of covariance parameters in partial linear model for longitudinal data

For longitudinal data, the within-subject dependence structure and covariance parameters may be of practical and theoretical interests. The estimation of covariance parameters has received much attention and been studied mainly in the framework of generalized estimating equations (GEEs). The GEEs method, however, is sensitive to outliers. In this paper, an alternative set of robust generalized estimating equations for both the mean and covariance parameters are proposed in the partial linear model for longitudinal data. The asymptotic properties of the proposed estimators of regression parameters, non-parametric function and covariance parameters are obtained. Simulation studies are conducted to evaluate the performance of the proposed estimators under different contaminations. The proposed method is illustrated with a real data analysis.     

Percentage points of LRC for testing equality of covariance matrices under intraclass correlation structure

The exact null distribution of the likelihood ratio test statistic for testing equality of covariance matrices of q compound symmetric Gaussian models (bivariate or trivariate) has been obtained and percentage points for q ? 5 have been computed. The inverse Mellin transform and calculus of residues have been used to derive these results.     

Bayesian variable selection in Poisson change-point regression analysis

In this article, we develop a Bayesian variable selection method that concerns selection of covariates in the Poisson change-point regression model with both discrete and continuous candidate covariates. Ranging from a null model with no selected covariates to a full model including all covariates, the Bayesian variable selection method searches the entire model space, estimates posterior inclusion probabilities of covariates, and obtains model averaged estimates on coefficients to covariates, while simultaneously estimating a time-varying baseline rate due to change-points. For posterior computation, the Metropolis-Hastings within partially collapsed Gibbs sampler is developed to efficiently fit the Poisson change-point regression model with variable selection. We illustrate the proposed method using simulated and real datasets.