A search of maximum generalized resolution quaternary-code designs via integer linear programming

Quaternary-code (QC) designs, an attractive class of nonregular fractional factorial designs, have received much attention due to their theoretical elegance and practical applicability. Some recent works of QC designs revealed their good properties over their regular counterparts under commonly used criteria. We develop an optimization tool that can maximize the generalized resolution of a QC design of a given size. The problem can be recast as an integer linear programming (ILP) problem through a linear simplification that combines the \(k\)- and \(a\)-equations, even though the generalized resolution does not linearly depend on the aliasing indexes. The ILP surprisingly improves a class of \((1/16)\)th-fraction QC designs with higher generalized resolutions. It also applies to obtain some \((1/64)\)th-fraction QC designs with maximum generalized resolutions, and these QC designs generally have higher generalized resolutions than the regular designs of the same size.     

Goodness-of-fit tests of generalized linear mixed models for repeated ordinal responses

Categorical longitudinal data are frequently applied in a variety of fields, and are commonly fitted by generalized linear mixed models (GLMMs) and generalized estimating equations models. The cumulative logit is one of the useful link functions to deal with the problem involving repeated ordinal responses. To check the adequacy of the GLMMs with cumulative logit link function, two goodness-of-fit tests constructed by the unweighted sum of squared model residuals using numerical integration and bootstrap resampling technique are proposed. The empirical type I error rates and powers of the proposed tests are examined by simulation studies. The ordinal longitudinal studies are utilized to illustrate the application of the two proposed tests.     

A Data Envelopment Analysis-Based Methodology Adopting Assurance Region Approach for Measuring Corporate Social Performance

Many research papers calculate corporate social performance (CSP) with the net score method, i.e., by subtracting the number of concerns from the number of strengths. Although widely adopted, this method implies, perhaps mistakenly, that each indicator is of equal importance and that however serious the social misconduct a firm may have engaged in, it can be completely offset by some positive social action. The method also implies that a given firm that has done both a lot of harm and a lot of good will have CSP similar to that of another firm that has done little harm and little good. In this study, however, we question the appropriateness of the net score method in terms of its ability to truly reflect CSP and truly identify the real effects of CSP on various characteristics. We therefore propose a data envelopment analysis-based methodology that adopts the assurance region approach for evaluating CSP, through which various CSP indicators are converted into a single composite measure of CSP. Our findings show that our proposed methodology consistently performs better than the net score method in evaluating CSP.

     

试论苯教的宗教性质及与藏区民间宗教的关系

文章主要对藏区古老的苯教的宗教属性和特征进行分析,认为在历史上,苯教作为一种宗教的名称出现时,其已经形成系统性的宗教体系,所以我们不能将之列为原始宗教。苯教一般被分为两类,即斯巴苯教与雍仲苯教。其中斯巴苯教与民间宗教有密切的关系,为藏区民间宗教的源流之一。     

礼物、关系和信任

本文试图通过关系/人情/信任三个具有本土意义的重要概念,来探讨中国社会中的礼物流动及其背后所隐含的人际意义。通过理论建构,笔者提出了一个“人际关系在送礼过程中的运作过程”模型。     

略论19世纪70-90年代的中国木船业

在近代,随着外国侵略势力的到来,中国航运业受到了很大的冲击,中国古老的航运工具———木船受到了排挤。但由于各种原因,在19世纪70—90年代这段时间里中国木船业并没有走向灭亡,而是持续的向前发展。     

Robust inference for estimating equations with nonignorably missing data based on SIR algorithm

Nonresponse is a very common phenomenon in survey sampling. Nonignorable nonresponse – that is, a response mechanism that depends on the values of the variable having nonresponse – is the most difficult type of nonresponse to handle. This article develops a robust estimation approach to estimating equations (EEs) by incorporating the modelling of nonignorably missing data, the generalized method of moments (GMM) method and the imputation of EEs via the observed data rather than the imputed missing values when some responses are subject to nonignorably missingness. Based on a particular semiparametric logistic model for nonignorable missing response, this paper proposes the modified EEs to calculate the conditional expectation under nonignorably missing data. We can apply the GMM to infer the parameters. The advantage of our method is that it replaces the non-parametric kernel-smoothing with a parametric sampling importance resampling (SIR) procedure to avoid nonparametric kernel-smoothing problems with high dimensional covariates. The proposed method is shown to be more robust than some current approaches by the simulations.     

Semiparametric regression of clustered current status data

This paper discusses regression analysis of clustered current status data under semiparametric additive hazards models. In particular, we consider the situation when cluster sizes can be informative about correlated failure times from the same cluster. To address the problem, we present estimating equation-based estimation procedures and establish asymptotic properties of the resulting estimates. Finite sample performance of the proposed method is assessed through an extensive simulation study, which indicates the procedure works well. The method is applied to a motivating data set from a lung tumorigenicity study.     

Inferences for a Partially Varying Coefficient Model With Endogenous Regressors

In this article, we propose a new class of semiparametric instrumental variable models with partially varying coefficients, in which the structural function has a partially linear form and the impact of endogenous structural variables can vary over different levels of some exogenous variables. We propose a three-step estimation procedure to estimate both functional and constant coefficients. The consistency and asymptotic normality of these proposed estimators are established. Moreover, a generalized F-test is developed to test whether the functional coefficients are of particular parametric forms with some underlying economic intuitions, and furthermore, the limiting distribution of the proposed generalized F-test statistic under the null hypothesis is established. Finally, we illustrate the finite sample performance of our approach with simulations and two real data examples in economics.     

Does a More Diversified Revenue Structure Lead to Greater Financial Capacity and Less Vulnerability in Nonprofit Organizations? A Bibliometric and Meta-Analysis

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations - This article explores how and to what extent revenue diversification and concentration strategies affect financial...