Are people in the South less intelligent than in the North? IQ and the North–South disparity in Italy

Socioeconomic disparity between North and South Italy has been recently explained by Lynn (2010) as the result of a lower intelligence quotient (IQ) of the Southern population. The present article discusses the procedure followed by Lynn, supplementing his data with new information on school assessments and per head regional income. Genetic North–South differences are then discussed on the basis of the most recent literature on the subject. The results do not confirm the suggested IQ-economy causal link.     

Effects of neuroticism,extraversion, circadian type and age on reported driver stress

Studies have examined effects of various personality variables, including extraversion and neuroticism, as well as age, on driver stress. However, the effect of the morningness-eveningness dimension (circadian type) on reported driver stress among commuters has not been investigated. This study aimed to assess the influence of circadian type as well as extraversion, neuroticism and age on driver stress. Participants were 101 Australian university administrative staff who completed the Eysenck Personality Questionnaire-Revised, the Morningness-Eveningness Questionnaire, and also morning and evening driving diaries as a state measure of driver stress each weekday for one week. Two standard multiple regressions assessed whether neuroticism, extraversion, circadian type and age predicted reported driver stress for morning and evening drives. Neuroticism, circadian type, and age predicted reported driver stress in the mornings but age mainly predicted driver stress in the evenings. A repeated measures ANOVA isolated differences between circadian types for weekday mornings and evenings. Reported driver stress differed for day of week and time of day according to circadian type. The three-way interaction suggested that driver stress is influenced by circadian type and that factors influence driver stress differentially between mornings and evenings. Circadian type appears to influence when driver stress affects individuals.     

Anderson's classification statistic based on a post-stratified training sample

The performance of Anderson's classification statistic based on a post-stratified random sample is examined. It is assumed that the training sample is a random sample from a stratified population consisting of two strata with unknown stratum weights. The sample is first segregated into the two strata by post-stratification. The unknown parameters for each of the two populations are then estimated and used in the construction of the plug-in discriminant. Under this procedure, it is shown that additional estimation of the stratum weight will not seriously affect the performance of Anderson's classification statistic. Furthermore, our discriminant enjoys a much higher efficiency than the procedure based on an unclassified sample from a mixture of normals investigated by Ganesalingam and McLachlan (1978).     

Comparison of estimators of the location parameter using pitman's closeness criterion

Necessary and sufficient conditions for a linear estimator to dominate another linear estimator of a location parameter under the Pitman's criterion of comparison are discussed. Consequently it is demonstrated that a linear biased estimator can not dominate a linear unbiased estimator under Pitman's criterion and that the sample mean is the Closest Linear Unbiased Estimator (CLUE). It is also shown that the ridge regression estimator with a known biasing constant can not dominate the ordinary least squares estimator. If an estimator δdominates an estimator δin the average loss sense then sufficient conditions are obtained under which δis also preferred over δunder Pitman's criterion. Further we obtain sufficient conditions under which preference under the Pitman's criterion will lead to preference under the mean squared error sense.     

Goodness-of-Fit Tests for Multiplicative Models with Dependent Data

Abstract.  Several classical time series models can be written as a regression model between the components of a strictly stationary bivariate process. Some of those models, such as the ARCH models, share the property of proportionality of the regression function and the scale function, which is an interesting feature in econometric and financial models. In this article, we present a procedure to test for this feature in a non-parametric context. The test is based on the difference between two non-parametric estimators of the distribution of the regression error. Asymptotic results are proved and some simulations are shown in the paper in order to illustrate the finite sample properties of the procedure.     

Bayesian estimation for the Pareto income distribution

The Bayes estimators of the Gini index, the mean income and the proportion of the population living below a prescribed income level are obtained in this paper on the basis of censored income data from a pareto income distribution. The said estimators are obtained under the assumptions of a two-parameter exponential prior distribution and the usual squared error loss function. This work is also extended to the case when the income data are grouped and the exact incomes for the individuals in the population are not available. The method for the assessment of the hyperparameters is also outlined. Finally, the results are generalized for the doubly truncated gamma prior distribution. Now deceased.     

关于高速铁路调度指挥系统稳定性的探讨

探讨了高速铁路调度所内调度系统的信息传递过程，采用控制论中系统方块图进行描述，计算出高铁调度系统的信息传递函数，然后求得调度系统的特征方程和特征根，通过分析特征根是否均有负实部来判断系统的稳定性。提出了描述调度系统控制精确度的稳态误差的计算方法。为今后高速铁路调度系统的稳定性的研究有所启发。     

P-value adjustment for multiple binary endpoints

The p-value-based adjustment of individual endpoints and the global test for an overall inference are the two general approaches for the analysis of multiple endpoints. Statistical procedures developed for testing multivariate outcomes often assume that the multivariate endpoints are either independent or normally distributed. This paper presents a general approach for the analysis of multivariate binary data under the framework of generalized linear models. The generalized estimating equations (GEE) approach is applied to estimate the correlation matrix of the test statistics using the identity and exchangeable working correlation matrices with the model-based as well as robust estimators. The objectives of the approaches are the adjustment of p-values of individual endpoints to identify the affected endpoints as well as the global test of an overall effect. A Monte Carlo simulation was conducted to evaluate the overall family wise error (FWE) rates of the single-step down p-value adjustment approach from two adjustment methods to three global test statistics. The p-value adjustment approach seems to control the FWE better than the global approach Applications of the proposed methods are illustrated by analyzing a carcinogenicity experiment designed to study the dose response trend for 10 tumor sites, and a developmental toxicity experiment with three malformation types: external, visceral, and skeletal.     

Nonparametric estimation of a change point in positive shift models

Six nonparametric estimators of the change point are compared via Monte Carlo simulation in positive shift models of widely different taillengths. It is found that the best estimator in terms of smallest mean-squared error depends on the taillength of the underlying distribution. Overall, an estimator of Lombard (1987) based on a Wilcoxon scores rank statistic is recommended.     

Estimation of the smaller and larger of two exponential location parameters

Assume independent random samples are drawn from two populations which are exponentially distributed with unknown location parameters and a common known scale parameter. We want to estimate the maximum and the minimum of the unknowo location paremeters. In this paper several estimators are proposed which are better than the natural estimations in terms of absolute bias and /or meaqn squared error.