亚当·斯密的正义观

亚当.斯密的正义思想丰富而深刻。斯密的正义具有强制性、自然性,是自由的前提和社会存在的基础;斯密的正义也具有其局限性。哈耶克的正义思想以及现代社会所倡导的分配正义、起点公正等都是对斯密思想的继承和创新。     

Modeling proportions and marginal counts simultaneously for clustered multinomial data with random cluster sizes

Clustered multinomial data with random cluster sizes commonly appear in health, environmental and ecological studies. Traditional approaches for analyzing clustered multinomial data contemplate two assumptions. One of these assumptions is that cluster sizes are fixed, whereas the other demands cluster sizes to be positive. Randomness of the cluster sizes may be the determinant of the within-cluster correlation and between-cluster variation. We propose a baseline-category mixed model for clustered multinomial data with random cluster sizes based on Poisson mixed models. Our orthodox best linear unbiased predictor approach to this model depends only on the moment structure of unobserved distribution-free random effects. Our approach also consolidates the marginal and conditional modeling interpretations. Unlike the traditional methods, our approach can accommodate both random and zero cluster sizes. Two real-life multinomial data examples, crime data and food contamination data, are used to manifest our proposed methodology.     

苏格兰启蒙运动的思想主题:市民社会的启蒙

苏格兰启蒙运动是18世纪启蒙运动的另一重镇。与法、德等欧陆启蒙运动不同,苏格兰启蒙运动是一场政治转型已然完成的后革命启蒙,它的主要关切不再是政治革命而是经济与社会的发展,不再是政治社会的建立而是市民社会的运行。在这一共同的思想主题下,休谟、斯密、弗格森等苏格兰启蒙思想家明确界分了市民社会与国家,并对市民社会中的人性、道德规范、社会化机制、经济行为、政治法律制度等问题进行了全面而深刻的思想启蒙与理论思考,形成了系统的市民社会理论。     

Chi-squared tests in kth-moment sufficient dimension reduction

We present a novel approach to sufficient dimension reduction for the conditional kth moments in regression. The approach provides a computationally feasible test for the dimension of the central kth-moment subspace. In addition, we can test predictor effects without assuming any models. All test statistics proposed in the novel approach have asymptotic chi-squared distributions.     

General ridge predictors in a mixed linear model

This paper mainly aims to put forward two estimators for the linear combination of fixed effects and random effects, and to investigate their properties in a general mixed linear model. First, we define the notion of a Type-I general ridge predictor (GRP) and obtain two sufficient conditions for a Type-I GRP to be superior over the best linear unbiased predictor (BLUP). Second, we establish the relationship between a Type-I GRP and linear admissibility, which results in the notion of Type-II GRP. We show that a linear predictor is linearly admissible if and only if it is a Type-II GRP. The superiority of a Type-II GRP over the BLUP is also obtained. Third, the problem of confidence ellipsoids based on the BLUP and Type-II GRP is investigated.     

Best quadratic predictions in finite populations

For a finite population and the resulting linear model Y=Xβ+e, the problem of the optimal invariant quadratic predictors including optimal invariant quadratic unbiased predictor and optimal invariant quadratic (potentially) biased predictor for the population quadratic quantities, f(H)=Y′HY , is of interest and has been previously considered in the literature for the case of HX=0. However, the special case does not contain all of situations at all. So, predicting f(H) in general situations may be of particular interest. In this paper, we make an effort to investigate how to offer a good predictor for f(H), not restricted yet to the mentioned case. Permutation matrix techniques play an important role in handling the process. The expected predictors are finally derived. In addition, we mention that the resulting predictors can be viewed as acceptable in all situations.     

On Mean Squared Prediction Error Estimation in Small Area Estimation Problems

For a general linear mixed normal model, a new linearized weighted jackknife method is proposed to estimate the mean squared prediction error (MSPE) of an empirical best linear unbiased predictor (EBLUP) of a general mixed effect. Different MSPE estimators are compared using a Monte Carlo simulation study.     

Robustness of Bayes Prediction Under Error-in-Variables Superpopulation Model

In this article, we consider Bayes prediction in a finite population under the simple location error-in-variables superpopulation model. Bayes predictor of the finite population mean under Zellner's balanced loss function and the corresponding relative losses and relative savings loss are derived. The prior distribution of the unknown location parameter of the model is assumed to have a non-normal distribution belonging to the class of Edgeworth series distributions. Effects of non normality of the “true” prior distribution and that of a possible misspecification of the loss function on the Bayes predictor are illustrated for a hypothetical population.     

On the Closed Form of Optimal Invariant Quadratic Predictors in Finite Populations

For a finite population and its linear model Y  =  X β +  e , the problem of deriving optimal invariant quadratic predictors including optimal invariant quadratic unbiased predictor (OIQUP) and optimal invariant quadratic (potentially) biased predictor (OIQBP) for the population quadratic quantities, Y ′ HY , is of interest and has been previously considered by Liu and Rong (2007 Liu , X. , Rong , J. ( 2007 ). Quadratic prediction problems in finite populations . Statist. Probab. Lett. 77 : 483 – 489 .[Crossref], [Web of Science ®] , [Google Scholar]). In this note, we mainly aim at motivating the problems of OIQUP and OIQBP by showing that the unique closed form of OIQUP and OIQBP is just the one given by Liu and Rong through permutation matrix techniques.