Effects of a Punitive Environment on Children's Executive Functioning: A Natural Experiment

Few studies have examined the influence of environmental factors on children's executive functioning (EF) performance. The present study examined the effects of a punitive vs. non‐punitive school environment on West African children's EF skills. Tasks included a ‘cool’ (relatively non‐affective) and ‘hot’ (relatively affective/motivational) version of three EF tasks: delay of gratification; gift delay; and dimensional change card sort. Children had more difficulties with the hot versions of the tasks than the cool versions, and older children outperformed younger children. After controlling for verbal ability (Peabody picture vocabulary test‐third edition), a consistent pattern of interaction between school and grade level emerged. Overall, kindergarten children in the punitive school performed no differently than their counterparts in the non‐punitive school. However, in grade 1, children in the punitive school performed significantly worse than their counterparts in the non‐punitive school. These results point to the need to consider interactions among discipline style, age, and internalization processes of self‐regulation to better understand environmental influences on EF development.     

个人教育收益率的估计与比较: 一个半参数方法

 本文运用一种变系数部分线性模型扩展了传统的明瑟人力资本收入函数,并提出了对明瑟函数中“平行性”假设的一种检验方法,以分析个人教育收益率如何受到工龄等因素的影响。通过对1991、2000以及2004等年度国家统计局城镇居民收入调查数据的分析,揭示出近二十年来我国城镇居民中不同工龄的人们的教育收益率存在明显区别,其中人教育收益率最高的始终是在1980年代中后期参加工作的群体。对导致我国教育收益率这种特殊现象的原因作了分析。     

Noninformative priors for the common mean in the bivariate normal distribution

In this paper, we consider some noninformative priors for the common mean in a bivariate normal population. We develop the first-order and second-order matching priors and reference priors. We find that the second-order matching prior is also an HPD matching prior, and matches the alternative coverage probabilities up to the second order. It turns out that derived reference priors do not satisfy a second-order matching criterion. Our simulation study indicates that the second-order matching prior performs better than the reference priors in terms of matching the target coverage probabilities in a frequentist sense. We also illustrate our results using real data.     

Noninformative priors for the normal variance ratio

In this paper, we consider noninformative priors for the ratio of variances in two normal populations. We develop first and second order matching priors. We find that the second order matching prior matches alternative coverage probabilities up to the second order and is also a HPD matching prior. It turns out that among the reference priors, only one-at-a-time reference prior satisfies a second order matching criterion. Our simulation study indicates that the one-at-a-time reference prior performs better than other reference priors in terms of matching the target coverage probabilities in a frequentist sense. This work is supported by Korea Research Foundation Grant (KRF-2004-002-C00041).     

Modifications of the Empirical Likelihood Interval Estimation with Improved Coverage Probabilities

The empirical likelihood (EL) technique has been well addressed in both the theoretical and applied literature in the context of powerful nonparametric statistical methods for testing and interval estimations. A nonparametric version of Wilks theorem (Wilks, 1938 Wilks , S. S. ( 1938 ). The large-sample distribution of the likelihood ratio for testing composite hypotheses . Annals of Mathematical Statistics 9 : 60 – 62 .[Crossref] , [Google Scholar]) can usually provide an asymptotic evaluation of the Type I error of EL ratio-type tests. In this article, we examine the performance of this asymptotic result when the EL is based on finite samples that are from various distributions. In the context of the Type I error control, we show that the classical EL procedure and the Student's t-test have asymptotically a similar structure. Thus, we conclude that modifications of t-type tests can be adopted to improve the EL ratio test. We propose the application of the Chen (1995 Chen , L. ( 1995 ). Testing the mean of skewed distributions . Journal of the American Statistical Association 90 : 767 – 772 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) t-test modification to the EL ratio test. We display that the Chen approach leads to a location change of observed data whereas the classical Bartlett method is known to be a scale correction of the data distribution. Finally, we modify the EL ratio test via both the Chen and Bartlett corrections. We support our argument with theoretical proofs as well as a Monte Carlo study. A real data example studies the proposed approach in practice.     

Multivariate Control Charts Based on Hybrid Novelty Scores

We propose a new nonparametric multivariate control chart that integrates a novelty score. The proposed control chart uses as its monitoring statistic a hybrid novelty score, calculated based on the distance to local observations as well as on the distance to the convex hull constructed by its neighbors. The control limits of the proposed control chart were established based on a bootstrap method. A rigorous simulation study was conducted to examine the properties of the proposed control chart under various scenarios and compare it with existing multivariate control charts in terms of average run length (ARL) performance. The simulation results showed that the proposed control chart outperformed both the parametric and nonparametric Hotelling's T ² control charts, especially in nonnormal situations. Moreover, experimental results with real semiconductor data demonstrated the applicability and effectiveness of the proposed control chart. To increase the capability to detect small mean shift, we propose an exponentially weighted hybrid novelty score control chart. Simulation results indicated that exponentially weighted hybrid score charts outperformed the hybrid novelty score based control charts.     

On the estimation of the true probability of a correct selection

For ranking and selection problems, the true probabiIity of a correct selection P(CS) is unknown even if a selection is made under the indifference-zone approach. Thus to estimate the true P(CS) some Bayes estimators and a bootstrap estimator are proposed for two normcal populations with common known variance. Also a bootstrap estimator and a bootstrap confidence interval are proposed for normal populations with common unknown variance. Some comparisons between proposed estimators and some other known estimators are made via Monte Carlo simulations.     

Bayesian estimation procedure in multiprocess non-linear dynamic generalized model

The multiprocess dynamic model provides a good framework for the modeling and analysis of the time series that contains outliers arid is subject to abrupt changes in pattern. In this paper we extend the multiprocess dynamic generalized linear model to allow for a known non-linear parameter evolution and predictor functions. This is done by approximating the non-linear function by a linear function based on a first order Taylor series expansions. This model has nice properties such as insensitivity to outliers and quick reaction to abrupt changes of pattern.     

The Effects of Detrending in Granger Causality Tests

Nonstationary time series are frequently detrended in empirical investigations by regressing the series on time or a function of time. The effects of the detrending on the tests for causal relationships in the sense of Granger are investigated using quarterly U.S. data. The causal relationships between nominal or real GNP and M1, inferred from the Granger–Sims tests, are shown to depend very much on, among other factors, whether or not series are detrended. Detrending tends to remove or weaken causal relationships, and conversely, failure to detrend tends to introduce or enhance causal relationships. The study suggests that we need a more robust test or a better definition of causality.