Addendum to the Paper,Bayesian D-Optimal and Model Robust Designs in Linear Regression Models

In the recent paper of Dette (1993) there are two figures missing which will be given in this addendum.     

Bounds for the variance of the graybill-deal estimator of the common mean of two normal distributions

In this note we derive sharp lower and upper bounds for the variance of the Graybill-Deal estimator of the common mean of two normal distributions with unknown variances when the sample sizes are not necessarily equal. We also derive similar bounds for the variance of the Brown-Cohen (1974) T _a(1) class of unbiased es-timators to which the Graybill-Deal estimator belongs. Further, we illustrate the sharpness of the bounds by numerical computations in the case of the Graybill-Deal estimator.     

Comparison of dispersions of mixtures of ordered distributions

In the paper we consider mixtures of unknown stochastically ordered distribution functions according to known mixing distribution functions. We provide optimal lower and upper bounds on ratios of general dispersion measures of such mixtures. The bounds do not depend on the particular form of dispersion measure. We present applications of the results in reliability theory, insurance mathematics, Bayesian statistics, and regression analysis.     

A new extension strategy on three-level factorials under wrap-around L2-discrepancy

Sequential experiment is an indispensable strategy and is applied immensely to various fields of science and engineering. In such experiments, it is desirable that a given design should retain the properties as much as possible when few runs are added to it. The designs based on sequential experiment strategy are called extended designs. In this paper, we have studied theoretical properties of such experimental strategies using uniformity measure. We have also derived a lower bound of extended designs under wrap-around L₂-discrepancy measure. Moreover, we have provided an algorithm to construct uniform (or nearly uniform) extended designs. For ease of understanding, some examples are also presented and a lot of sequential strategies for a 27-run original design are tabulated for practice.     

复合行动论初探

理性由于其有限性 ,造成了人类行动的复合性特点。在任何行动中起作用的所有因素 ,既有“理性所及因素”又有“理性未及因素” ,人类行动均是这两类因素共同起作用的结果。由于这两种因素所起的作用不同 ,便造成了人类行动既带有理性所及的特点又带有理性未及的特点 ,这种特点决定了人类行动是理性所及行动与理性未及行动的复合统一 ,即是一种复合行动。这种行动理论可称之为复合行动理论     

大学跨越式发展的条件与路径——以国内外8所大学的发展为例

大学跨越式发展首先必须具备一定条件：一名锐意创新的校长,一个先进的办学理念,一个共同做事的好氛围,一批国内外知名的教授。其次,大学跨越式发展要遵循一定的路径,这种路径则因校制宜。     

自主创新——实现本土化经济跨越式发展的有效路径

从自主创新理论的形成和发展、自主创新必须立足国情进行、自主创新的实现方式、自主创新———支撑起中国经济的跨越式发展等四个部分深刻剖析了自主创新的深刻涵义,并从自主创新的路径选择,自主创新实现的教育、制度、动力、文化、渠道、服务等环境支持着重分析了自主创新的实现方式。     

谦逊的理性:教育学应有的理论品格--关于教育理论原创的前提性思考

教育理论原创首先意味着教育学要成为自身.教育学缘何"殖民化"就逻辑地成为教育理论原创这一课题必需解答的问题.目前的教育学存在着一种狂妄的理性,没有区分教育学的实质研究对象和形式研究对象,成为一门没有限度的学科.其逻辑结果是移用其他学科的概念、分析框架、思维方式等,从而被殖民化.这是原创性教育理论缺失的前提性原因.教育理论原创的产生需要一种谦逊的理性,谦逊的理性规约下的教育学具有真正意义上的学科尊严.     

Efficient Semiparametric Estimation of Quantile Treatment Effects

This paper develops estimators for quantile treatment effects under the identifying restriction that selection to treatment is based on observable characteristics. Identification is achieved without requiring computation of the conditional quantiles of the potential outcomes. Instead, the identification results for the marginal quantiles lead to an estimation procedure for the quantile treatment effect parameters that has two steps: nonparametric estimation of the propensity score and computation of the difference between the solutions of two separate minimization problems. Root‐N consistency, asymptotic normality, and achievement of the semiparametric efficiency bound are shown for that estimator. A consistent estimation procedure for the variance is also presented. Finally, the method developed here is applied to evaluation of a job training program and to a Monte Carlo exercise. Results from the empirical application indicate that the method works relatively well even for a data set with limited overlap between treated and controls in the support of covariates. The Monte Carlo study shows that, for a relatively small sample size, the method produces estimates with good precision and low bias, especially for middle quantiles.