Forecast mean squared error reductionin the VAR(1) process

When VAR models are used to predict future outcomes, the forecast error can be substantial. Through imposition of restrictions on the off-diagonal elements of the parameter matrix, however, the information in the process may be condensed to the marginal processes. In particular, if the cross-autocorrelations in the system are small and only a small sample is available, then such a restriction may reduce the forecast mean squared error considerably.

In this paper, we propose three different techniques to decide whether to use the restricted or unrestricted model, i.e. the full VAR(1) model or only marginal AR(1) models. In a Monte Carlo simulation study, all three proposed tests have been found to behave quite differently depending on the parameter setting. One of the proposed tests stands out, however, as the preferred one and is shown to outperform other estimators for a wide range of parameter settings.     

A closer look at de-aliasing effects using an efficient foldover technique

Foldover techniques are used to reduce the confounding when some important effects (usually lower order effects) cannot be estimated independently. This article develops an efficient foldover mechanism for symmetric or asymmetric designs, whether regular or nonregular. In this paper, we take the uniformity criteria (UC) as the optimality measures to construct the optimal combined designs (initial design plus its corresponding foldover design) which have better capability of estimating lower order effects. The relationship between any initial design and its combined design is studied. A comparison study between the combined designs via different UC is provided. Equivalence between any combined design and its complementary combined design is investigated, which is a very useful constraint that reduce the search space. Using our results as benchmarks, we can implement a powerful algorithm for constructing optimal combined designs. Our work covers as well as gives results better than recent works of about 20 articles in the last few years as special cases. So this article is a good reference for constructing effective designs.     

Model selection criteria in beta regression with varying dispersion

We address the issue of model selection in beta regressions with varying dispersion. The model consists of two submodels, namely: for the mean and for the dispersion. Our focus is on the selection of the covariates for each submodel. Our Monte Carlo evidence reveals that the joint selection of covariates for the two submodels is not accurate in finite samples. We introduce two new model selection criteria that explicitly account for varying dispersion and propose a fast two step model selection scheme which is considerably more accurate and is computationally less costly than usual joint model selection. Monte Carlo evidence is presented and discussed. We also present the results of an empirical application.     

Evaluating modified generalized information criterion in presence of multicollinearity

When there are many explanatory variables in the regression model, there is a chance that some of these are intercorrelated. This is where the problem of multicollinearity creeps in due to which precision and accuracy of the coefficients is marred, and the quest to find the best model becomes tedious. To tackle such a situation, Model selection criteria are applied for selecting the best model that fits the data. Current study focuses on the evaluation of the four unmodified and four modified versions of generalized information criteria—Akaike Information Criterion, Schwarz's Bayes Information Criteria, Hannan-Quinn Information Criterion, and Akaike Information Criterion corrected for small samples. A simulation study using SAS software was carried out in order to compare the unmodified and modified versions of the generalized information criteria and to discover the best version amongst the four modified model selection criteria, for identifying the best model, when the collinearity assumption is violated. For the proposed simulation, two samples of size 50 and 100, for three explanatory variables X₁, X₂, and X₃, are drawn from Normal distribution. Two situations of collinearity violations between X₁ and X₂ are looked into, first when ρ = 0.6 and second when ρ = 0.8. The outcomes of the simulations are displayed in the tables along with visual representations. The results revealed that modified versions of the generalized information criteria are more sensitive in identifying models marred with high multicollinearity as compared to the unmodified generalized information criteria.     

A multicriteria sorting approach based on data envelopment analysis for R&D project selection problem

In this paper, multiple criteria sorting methods based on data envelopment analysis (DEA) are developed to evaluate research and development (R&D) projects. The weight intervals of the criteria are obtained from Interval Analytic Hierarchy Process and employed as the assurance region constraints of models. Based on data envelopment analysis, two threshold estimation models, and five assignment models are developed for sorting. In addition to sorting, these models also provide ranking of the projects. The developed approach and the well-known sorting method UTADIS are applied to a real case study to analyze the R&D projects proposed to a grant program executed by a government funding agency in 2009. A five level R&D project selection criteria hierarchy and an assisting point allocation guide are defined to measure and quantify the performance of the projects. In the case study, the developed methods are observed to be more stable than UTADIS.     

少数民族高考加分政策的公正性探究

少数民族高考加分政策实施多年,其公正性屡遭质疑,根本原因在于对该政策的根据缺乏深入而系统的认识。事实上,理性约定和平等原则构成其法律基础,循环累积因果论和帕累托最优为其提供经济学上的理论依据,唯有从这两方面厘清该政策的正当性根据,方可消除种种分歧以及在执行和实施过程中的异化和不合理现象。     

Risk-Informed Requirements for Design and Assessment of Structures Under Temporary Use

The relatively high failure rates, with important consequences in many cases, suggest that the implicitly acceptable risk levels corresponding to temporary civil engineering structures and activities might exceed the bounds of normally acceptable levels associated with different societal activities. Among other reasons, this may be attributed to the lack of a rational approach for the assessment of risks associated with the different technologies supporting these activities in general, and for structures in particular. There is a need for establishing appropriate target reliability levels for structures under temporary use taking into account specific circumstances such as reduced risk exposure times. This issue is being addressed in this article. Acceptance criteria for building-structure-related risks to persons obtained in prior studies are adapted to the special circumstances of nonpermanent risk exposure. Thereby, the general principle followed is to maintain the same risk levels per time unit as for permanently occupied buildings. The adaptation is based on the statistical annual fatality rate, a life safety risk metric that allows for a consistent comparison of risks across different societal activities and technologies. It is shown that the target reliability indices taking account of the temporary use of buildings might be significantly higher than the values suggested for permanently used structures.     

Relevance of Risk Predictions Derived from a Chronic Species Sensitivity Distribution with Cadmium to Aquatic Populations and Ecosystems

Criteria to protect aquatic life are intended to protect diverse ecosystems, but in practice are usually developed from compilations of single‐species toxicity tests using standard test organisms that were tested in laboratory environments. Species sensitivity distributions (SSDs) developed from these compilations are extrapolated to set aquatic ecosystem criteria. The protectiveness of the approach was critically reviewed with a chronic SSD for cadmium comprising 27 species within 21 genera. Within the data set, one genus had lower cadmium effects concentrations than the SSD fifth percentile‐based criterion, so in theory this genus, the amphipod Hyalella, could be lost or at least allowed some level of harm by this criteria approach. However, population matrix modeling projected only slightly increased extinction risks for a temperate Hyalella population under scenarios similar to the SSD fifth percentile criterion. The criterion value was further compared to cadmium effects concentrations in ecosystem experiments and field studies. Generally, few adverse effects were inferred from ecosystem experiments at concentrations less than the SSD fifth percentile criterion. Exceptions were behavioral impairments in simplified food web studies. No adverse effects were apparent in field studies under conditions that seldom exceeded the criterion. At concentrations greater than the SSD fifth percentile, the magnitudes of adverse effects in the field studies were roughly proportional to the laboratory‐based fraction of species with adverse effects in the SSD. Overall, the modeling and field validation comparisons of the chronic criterion values generally supported the relevance and protectiveness of the SSD fifth percentile approach with cadmium.     

Prospect Theory and Limit Lines

Risk acceptance criteria in the form of limit lines are investigated in the context of prospect theory. This theory departs from utility theory in several respects, an important one being the use of weights other than probabilities in the evaluation of the expected impact of uncertain outcomes. Hypothetical functions reflecting certain attitudes toward consequences and rare events are developed and combined to produce several limit lines.     

关于中国古典戏曲中悲剧标准的探讨

中国古典戏曲中究竟有没有悲剧?这是一个争论了将近一个世纪的问题。一百年来,关于这个问题的观点差异较大,难有定论,其主要原因是判断悲剧的标准难于确定。"悲剧"作为一个美学范畴,有它的本质特征和标准。我们在对有关悲剧理论进行史的考察,对一些公认的悲剧作品进行分析后,才能抽绎出悲剧的本质特征。悲剧精神从美学本质上对悲剧进行定位,可作为判断悲剧的一个根本标准。