Best linear unbiased quantile estimators of the logistic distribution using order statistics

This paper deals with the problem of estimating the Quantiles Q(ξ), with ξ=0.01, 0.05(0.05)0.95, 0.99 of the logistic distribution by using order statistics in small samples. Linear unbiased estimators with minimum variance BLUE based on ordered observations are constructed for sample size n = 2(1)10, 15 and 20.     

The bivariate logarithmic series distribution

The bivariate logarithmic series distribution was introduced by Subrahmaniam (1966) as a Fisher-limit to the bivariate negative binomial distribution. The present paper considers the properties of the distribution along with various models giving rise to it. Problems of estimation and the goodness-of-fit are examined. Methods for simulating the distribution are developed and illusuated.     

Decriminalization and Marijuana Smoking Prevalence: Evidence From Australia

This article uses the 2001 National Drug Strategy Household Survey to assess the impact of marijuana decriminalization policy on marijuana smoking prevalence in Australia. Both parametric and nonparametric methods are used. The parametric approach includes endogenous probit switching, two-part, sample selection, and standard dummy variable models, while the nonparametric approach uses propensity score stratification matching. Specification analyses are also conducted. A nonparametric kernel-based test is constructed to select between parametric and nonparametric models, and the likelihood ratio test is used to choose among parametric models. Our analyses favor the endogenous switching model where decriminalization increases the probability of smoking by 16.2%.     

高职院校一年级新生语言感知学习风格调研与分析

不同的学习者认知和处理信息的方式各不相同,他们的感知学习风格偏向也不尽相同。对连云港职业技术学院2010级200名新生的感知学习风格进行了定量研究,采用SPSS统计软件对学生的感知学习风格偏向与性别、文理科及高考英语成绩等变量进行了t检验分析,检验结果表明不同群体之间存在一定的差异性。     

博士研究生英语入学考试质量解析—案例分析

博士研究生入学英语考试是一项国家级的重要考试,然而,有关此类考试质量的情况却没有引起相应的关注。本文通过对三份博士研究生入学英语考试试卷进行统计学分析,并按照测试理论标准进行检验,提示此类试卷在结构、内容、难易度等方面需要规范化、标准化,使考试对考生的语言知识能力做出科学、客观、公正的评价。     

Bias reduction of maximum likelihood estimates for a modified skew-normal distribution

ABSTRACT

This paper presents a modified skew-normal (SN) model that contains the normal model as a special case. Unlike the usual SN model, the Fisher information matrix of the proposed model is always non-singular. Despite of this desirable property for the regular asymptotic inference, as with the SN model, in the considered model the divergence of the maximum likelihood estimator (MLE) of the skewness parameter may occur with positive probability in samples with moderate sizes. As a solution to this problem, a modified score function is used for the estimation of the skewness parameter. It is proved that the modified MLE is always finite. The quasi-likelihood approach is considered to build confidence intervals. When the model includes location and scale parameters, the proposed method is combined with the unmodified maximum likelihood estimates of these parameters.     

On testing inference in beta regressions

This article deals with testing inference in the class of beta regression models with varying dispersion. We focus on inference in small samples. We perform a numerical analysis in order to evaluate the sizes and powers of different tests. We consider the likelihood ratio test, two adjusted likelihood ratio tests proposed by Ferrari and Pinheiro [Improved likelihood inference in beta regression, J. Stat. Comput. Simul. 81 (2011), pp. 431–443], the score test, the Wald test and bootstrap versions of the likelihood ratio, score and Wald tests. We perform tests on the parameters that index the mean submodel and also on the parameters in the linear predictor of the precision submodel. Overall, the numerical evidence favours the bootstrap tests. It is also shown that the score test is considerably less size-distorted than the likelihood ratio and Wald tests. An application that uses real (not simulated) data is presented and discussed.     

A Corrected Pseudo-score Approach for Additive Hazards Model with Longitudinal Covariates Measured with Error

In medical studies, it is often of interest to characterize the relationship between a time-to-event and covariates, not only time-independent but also time-dependent. Time-dependent covariates are generally measured intermittently and with error. Recent interests focus on the proportional hazards framework, with longitudinal data jointly modeled through a mixed effects model. However, approaches under this framework depend on the normality assumption of the error, and might encounter intractable numerical difficulties in practice. This motivates us to consider an alternative framework, that is, the additive hazards model, about which little research has been done when time-dependent covariates are measured with error. We propose a simple corrected pseudo-score approach for the regression parameters with no assumptions on the distribution of the random effects and the error beyond those for the variance structure of the latter. The estimator has an explicit form and is shown to be consistent and asymptotically normal. We illustrate the method via simulations and by application to data from an HIV clinical trial.     

Ability and rates of return to schooling—making use of the Swedish enlistment battery test

Using the Swedish military enlistment test, this paper estimates the return to schooling for individuals belonging to different parts of the ability distribution. It also attempts to predict whether an endogenous test score causes bias in the “ability-specific” returns to schooling that varies with the test score. A significant finding is that a higher score in the test is associated with a higher return to schooling, but that the positive association is diminishing in the test score. In general, the bias in the ability-specific returns to schooling does not seem to vary with the test score level.

Measuring multiple discrimination through a survey-based methodology

This paper focuses on the concept of multiple discrimination and its measurement through survey methods. The study was designed as a quasi-experimental comparison of survey mode effects on the quality of discrimination measurement: the traditional ‘face-to-face’ survey, the conventional self-completed mode and CAWI (finally deleted due to its non-comparability). Consistent with our hypothesis, some support was obtained for the social desirability bias and survey mode effects: 1) self-administration of questionnaires favours the declaration of discriminatory attitudes and personal experiences of discrimination; 2) the effect of privacy is greater in direct indicators of discriminatory attitudes; 3) perceptions and experiences of discrimination are more frequently reported by highly educated respondents. Nevertheless, contrary to our expectations, less educated respondents are also affected by survey mode and continue to be underrepresented in self-completed methods. The current research aims to serve as a basis for further research in this area.