Collective corruption–How to live with it: Towards a projection theory of post-crisis corruption perpetuation

This article draws on social psychology to explore the unconscious cognitive processes allowing for perpetuation of collective corruption in organisations in the aftermath of crises. In particular, we argue that, when faced with the cognitive dissonance produced by exposed collective corruption, and having to choose between changing behaviour or changing cognition, projection theory provides support for the latter. Thus, we identify the role of projection theory in overcoming cognitive dissonance in groups by projecting blame on to their leaders while continuing practices of corruption. These insights contribute to our understanding of perpetuation of collective corruption in organisations as well as at a societal level.     

欧盟《外国直接投资审查框架条例》评析

最近生效的欧盟《建立外国直接投资审查框架的条例》赋予欧盟委员会新的权力,使其可以以安全和公共秩序为由对进入欧盟任一成员国的外国投资进行审查。条例结束了外资安全审查纯属成员国事务的历史,削弱了欧盟成员国的外资批准权。条例生效后,我国在关键技术、关键基础设施等领域以及由国有企业进行的对欧直接投资可能在准入方面受到不利影响。本文采用双向欧洲化的研究视角分析了该制度的“因”与“果”。本文通过主动欧洲化的视角分析指出外资安全审查从成员国层面提升到欧盟层面是德法两国将其国内政策投射到欧盟的结果,其深层原因是这两个欧盟大国因中欧竞争和“16+1合作”而产生了焦虑,力图通过增设欧盟制度来影响其他成员国的外资准入政策。文章通过受动欧洲化的视角指出,条例实施后成员国的外资安全制度会向趋同方向发展,但其中也存在不确定性与挑战,当欧盟与其成员国在发展与安全的关系上持不同观点时,挑战会比较尖锐。欧盟与其将经济和发展问题上升为政治风险,不如帮助成员国以发展来化解风险。     

Incomplete row-column designs with factorial treatment structure for estimating main effects with full efficiency

In this paper, we propose two methods of constructing row-column designs for factorial experiments. The constructed designs have orthogonal factorial structure with balance and permits estimation of main effects with full efficiency.     

An orthogonal arrays approach to robust parameter designs methodology

Robust parameter design methodology was originally introduced by Taguchi [14 Taguchi, G. 1986. Introduction to Quality Engineering: Designing Quality Into Products and Process, Tokyo: Asian Productivity Organization.  [Google Scholar]] as an engineering methodology for quality improvement of products and processes. A robust design of a system is one in which two different types of factors are varied; control factors and noise factors. Control factors are variables with levels that are adjustable, whereas noise factors are variables with levels that are hard or impossible to control during normal conditions, such as environmental conditions and raw-material properties. Robust parameter design aims at the reduction of process variation by properly selecting the levels of control factors so that the process becomes insensitive to changes in noise factors. Taguchi [14 Taguchi, G. 1986. Introduction to Quality Engineering: Designing Quality Into Products and Process, Tokyo: Asian Productivity Organization.  [Google Scholar] 15 Taguchi, G. 1987. System of Experimental Design, Vol. I and II, New York: UNIPUB.  [Google Scholar]] proposed the use of crossed arrays (inner–outer arrays) for robust parameter design. A crossed array is the cross-product of an orthogonal array (OA) involving control factors (inner array) and an OA involving noise factors (outer array). Objecting to the run size and the flexibility of crossed arrays, several authors combined control and noise factors in a single design matrix, which is called a combined array, instead of crossed arrays. In this framework, we present the use of OAs in Taguchi's methodology as a useful tool for designing robust parameter designs with economical run size.     

Projection Properties of Certain Three-Level Main Effect Plans with Quantitative Factors

ABSTRACT

Orthogonal arrays are used as screening designs to identify active main effects, after which the properties of the subdesign for estimating these effects and possibly their interactions become important. Such a subdesign is known as a “projection design”. In this article, we have identified all the geometric non isomorphic projection designs of an OA(27,13,3,2), an OA(18,7,3,2) and an OA(36,13,3,2) into k = 3,4, and 5 factors when they are used for screening out active quantitative experimental factors, with regard to the prior selection of the middle level of factors. We use the popular D-efficiency criterion to evaluate the ability of each design found in estimating the parameters of a second order model.     

Asymmetrical Orthogonal Arrays With Run Size 100

Nowadays orthogonal arrays play important roles in statistics and other fields. Usual difference matrices are essential for the construction of many symmetrical or a few asymmetrical orthogonal arrays. But there are also asymmetrical orthogonal arrays which can not be obtained by the usual difference matrices. In order to construct these asymmetrical orthogonal arrays, a class of special matrices were discovered from the orthogonal decompositions of projection matrices. In this article, an interesting equivalent relationship between orthogonal arrays and the generalized difference matrices is presented. As an application, a lot of new orthogonal arrays of run size 100 have been constructed.     

General orthogonal designs for parameter estimation of ANOVA models under weighted sum-to-zero constraints

ABSTRACT

Traditional studies on optimal designs for ANOVA parameter estimation are based on the framework of equal probabilities of appearance for each factor's levels. However, this premise does not hold in a variety of experimental problems, and it is of theoretical and practical interest to investigate optimal designs for parameters with unequal appearing odds. In this paper, we propose a general orthogonal design via matrix image, in which all columns’ matrix images are orthogonal with each other. Our main results show that such designs have A- and E-optimalities on the estimation of ANOVA parameters which have unequal appearing odds. In addition, we develop two simple methods to construct the proposed designs. The optimality of the design is also validated by a simulation study.     

A target distribution model for nonparametric density estimation

In this paper a model is proposed which represents a wide class of continuous distributions. It is shown how the parameters of this model can be estimated leading to a distribution estimator and a corresponding density estimator. An important property of this estimator is that it can be structured to reflect a priori knowledge of the unknown distribution.

Finally, some examples are shown and some comparisons made with kernel and orthogonal series estimators.     

Experimental design for four mixture components with process variables

The experimental design to model the response of a mixture in four components in the presence of process variables is considered. Two different blocks of blends that are orthogonal for linear or quadratic blending are D-optimized. The two orthogonal blocks of blends are generalized and D-optimized in some cases (and possibly Doptimized in others) to deal with restrictions on the blending component proportions. The pair of orthogonal D-optimal blocks of blends can be used with an arbitrary number of process variables, and requires a reduced number of observations.     

Iterated Feasible Generalized Least-Squares Estimation of Augmented Dynamic Panel Data Models

This article provides the large sample distribution of the iterated feasible generalized least-squares (IFGLS) estimator of an augmented dynamic panel data model. The regressors in the model include lagged values of the dependent variable and may include other explanatory variables that, while exogenous with respect to the time-varying error component, may be correlated with an unobserved time-invariant component. The article compares the finite sample properties of the IFGLS estimator to that of GMM estimators using both simulated and real data and finds that the IFGLS estimator compares favorably.