Flexible Bivariate Count Data Regression Models

The article develops a semiparametric estimation method for the bivariate count data regression model. We develop a series expansion approach in which dependence between count variables is introduced by means of stochastically related unobserved heterogeneity components, and in which, unlike existing commonly used models, positive as well as negative correlations are allowed. Extensions that accommodate excess zeros, censored data, and multivariate generalizations are also given. Monte Carlo experiments and an empirical application to tobacco use confirms that the model performs well relative to existing bivariate models, in terms of various statistical criteria and in capturing the range of correlation among dependent variables. This article has supplementary materials online.     

Modified ridge analyses under nonstandard

A well-known procedure for the optimization of a second-degree response function over a spherical region of interest is that of ridge analysis. Khuri and Myers (1979) introduced a modification of this procedure by incorporating a certain constraint on the prediction variance. Both procedures, however, assume that the response variable has a constant variance throughtout the experimental region. In the present article, we consider two extensions to Khuri and Myers modifioed ridge analysis. The first extension relaxes the constant variance assumption. In the second extension. generalised linear models are used instead of the trasitional linear model. which are commenly used in responce variables that are not necessaily continusly distribution, including these have discreate distributions, Two examples are presented to illustrate the implementation of the proposed extensions.     

Heterogeneity and study size in random-effects meta-analysis

It is well known that heterogeneity between studies in a meta-analysis can be either caused by diversity, for example, variations in populations and interventions, or caused by bias, that is, variations in design quality and conduct of the studies. Heterogeneity that is due to bias is difficult to deal with. On the other hand, heterogeneity that is due to diversity is taken into account by a standard random-effects model. However, such a model generally assumes that heterogeneity does not vary according to study-level variables such as the size of the studies in the meta-analysis and the type of study design used. This paper develops models that allow for this type of variation in heterogeneity and discusses the properties of the resulting methods. The models are fitted using the maximum-likelihood method and by modifying the Paule–Mandel method. Furthermore, a real-world argument is given to support the assumption that the inter-study variance is inversely proportional to study size. Under this assumption, the corresponding random-effects method is shown to be connected with standard fixed-effect meta-analysis in a way that may well appeal to many clinicians. The models and methods that are proposed are applied to data from two large systematic reviews.     

A Comment on Salamon and Anheier's “Social Origins of Civil Society”

In this paper we examine Salamon and Anheier's characterization and testing of alternative theories of the size and scope of the nonprofit sector in different countries. We identify various nuances and refinements of these theories, and we suggest ways in which their validation through statistical testing can be extended and improved. Finally, we offer additional avenues of research that could productively exploit the important cross-national data set assembled by the Johns Hopkins Project.     

农民工务工地公共服务感知状况测度

基于微观调查数据,运用模糊数学方法测度农民工对务工地公共服务的感知状况,并分析了异质要素对农民工公共服务感知差异的影响。研究显示,农民工务工地公共服务感知状况的总模糊指数为0.134,反映出该群体在务工地的公共服务体验较差,尤其是在就业服务、社会服务、社会保障上的感知体验仍远低于预期。同时,农民工群体内部出现了基于个体特征和经济地位区分而形成的公共服务感知上的分异。为此政策层面应客观认识农民工在不同维度公共服务上的差异体验,兼顾不同特征农民工的公共服务需求,推动针对这一群体在城市务工地的公共服务的有效供给。     

Attracting talent through diversity at the top: The impact of TMT diversity and firms' efforts to promote diversity on employer attractiveness

At a time where firms encounter a “race for talent”, it is crucial for many MNCs to present themselves as attractive employers. Failing to position themselves in the international labor market can reduce firms' ability to acquire valuable international human capital, thereby generating disadvantageous organizational effects. Against this backdrop, drawing on signaling theory and employer branding literature, our paper aims to shed light on the association between nationality diversity in the executive suite and MNCs' employer attractiveness. Our lines of argument build on the notion that top management team composition can affect MNCs' efforts to promote diversity among their employees. This focus on diversity, in turn, can affect MNCs' employer attractiveness. Examining firms from various European countries, we find that top management team nationality diversity is positively associated with firms' employer attractiveness for foreign job seekers. We also show that a firm's efforts to promote diversity mediate the relationship between TMT nationality diversity and employer attractiveness. We therefore advance the academic debate on diversity as an employer branding tool and a means to enhance employer attractiveness. In practical terms, we also provide valuable insights for firms wishing to transform into (more) diverse entities.     

The Singularity of the Information Matrix of the Mixed Proportional Hazard Model

This paper presents new identification conditions for the mixed proportional hazard model. In particular, the baseline hazard is assumed to be bounded away from 0 and ∞ near t = 0. These conditions ensure that the information matrix is nonsingular. The paper also presents an estimator for the mixed proportional hazard model that converges at rate N^−1/2.     

A linear model-based test for the heterogeneity of conditional correlations

Current methods of testing the equality of conditional correlations of bivariate data on a third variable of interest (covariate) are limited due to discretizing of the covariate when it is continuous. In this study, we propose a linear model approach for estimation and hypothesis testing of the Pearson correlation coefficient, where the correlation itself can be modeled as a function of continuous covariates. The restricted maximum likelihood method is applied for parameter estimation, and the corrected likelihood ratio test is performed for hypothesis testing. This approach allows for flexible and robust inference and prediction of the conditional correlations based on the linear model. Simulation studies show that the proposed method is statistically more powerful and more flexible in accommodating complex covariate patterns than the existing methods. In addition, we illustrate the approach by analyzing the correlation between the physical component summary and the mental component summary of the MOS SF-36 form across a fair number of covariates in the national survey data.