Quantile regression for robust estimation and variable selection in partially linear varying-coefficient models

In this paper, we develop a new estimation procedure based on quantile regression for semiparametric partially linear varying-coefficient models. The proposed estimation approach is empirically shown to be much more efficient than the popular least squares estimation method for non-normal error distributions, and almost not lose any efficiency for normal errors. Asymptotic normalities of the proposed estimators for both the parametric and nonparametric parts are established. To achieve sparsity when there exist irrelevant variables in the model, two variable selection procedures based on adaptive penalty are developed to select important parametric covariates as well as significant nonparametric functions. Moreover, both these two variable selection procedures are demonstrated to enjoy the oracle property under some regularity conditions. Some Monte Carlo simulations are conducted to assess the finite sample performance of the proposed estimators, and a real-data example is used to illustrate the application of the proposed methods.     

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.     

Robust modal estimation and variable selection for single-index varying-coefficient models

In this article, we present a new efficient iteration estimation approach based on local modal regression for single-index varying-coefficient models. The resulted estimators are shown to be robust with regardless of outliers and error distributions. The asymptotic properties of the estimators are established under some regularity conditions and a practical modified EM algorithm is proposed for the new method. Moreover, to achieve sparse estimator when there exists irrelevant variables in the index parameters, a variable selection procedure based on SCAD penalty is developed to select significant parametric covariates and the well-known oracle properties are also derived. Finally, some numerical examples with various distributed errors and a real data analysis are conducted to illustrate the validity and feasibility of our proposed method.     

Variable selection for semiparametric proportional hazards model under progressive Type-II censoring

Variable selection is an effective methodology for dealing with models with numerous covariates. We consider the methods of variable selection for semiparametric Cox proportional hazards model under the progressive Type-II censoring scheme. The Cox proportional hazards model is used to model the influence coefficients of the environmental covariates. By applying Breslow’s “least information” idea, we obtain a profile likelihood function to estimate the coefficients. Lasso-type penalized profile likelihood estimation as well as stepwise variable selection method are explored as means to find the important covariates. Numerical simulations are conducted and Veteran’s Administration Lung Cancer data are exploited to evaluate the performance of the proposed method.     

Sparse Bayesian variable selection in multinomial probit regression model with application to high-dimensional data classification

Here we consider a multinomial probit regression model where the number of variables substantially exceeds the sample size and only a subset of the available variables is associated with the response. Thus selecting a small number of relevant variables for classification has received a great deal of attention. Generally when the number of variables is substantial, sparsity-enforcing priors for the regression coefficients are called for on grounds of predictive generalization and computational ease. In this paper, we propose a sparse Bayesian variable selection method in multinomial probit regression model for multi-class classification. The performance of our proposed method is demonstrated with one simulated data and three well-known gene expression profiling data: breast cancer data, leukemia data, and small round blue-cell tumors. The results show that compared with other methods, our method is able to select the relevant variables and can obtain competitive classification accuracy with a small subset of relevant genes.     

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.     

Modern Traditionalism: Consanguineous Marriage in Qatar

Consanguineous marriage, or matrimony between biological relatives, is commonplace and on the rise in Qatar, an ethnically heterogeneous Middle Eastern country. Previous studies have demonstrated that modernization often reduces traditional forms of marriage, such as arranged and consanguineous matrimony. Qatar's rapid modernization, however, has coincided with increased rates of endogamy. In this article, the authors examine the social processes that shape normative consanguineous marriage formation and progression in Qatar. On the basis of in‐depth interviews (n = 35) with men and women engaged or married to a cousin, they detail the categorical schemas that lead to consanguineous matrimony: reservation, joking, filtering, engagement, and courtship. In describing these social exchanges, the authors illustrate 2 key features embedded within them: (a) their distinct tempo and (b) how participants frame their experiences using discourses of romantic love. Reflecting larger cultural practices and attitudes in Qatar, the authors found an amalgam of contemporary and conventional that they label modern traditionalism.     

Norms,status and the dynamics of advice networks: A case study

The issue of the influence of norms on behavior is as old as sociology itself. This paper explores the effect of normative homophily (i.e. “sharing the same normative choices”) on the evolution of the advice network among lay judges in a courthouse. 0020 and 0025 social exchange theory suggests that members select advisors based on the status of the advisor. Additional research shows that members of an organization use similarities with others in ascribed, achieved or inherited characteristics, as well as other kinds of ties, to mitigate the potentially negative effects of this strong status rule. We elaborate and test these theories using data on advisor choice in the Commercial Court of Paris. We use a jurisprudential case about unfair competition (material and “moral” damages), a case that we submitted to all the judges of this court, to test the effect of normative homophily on the selection of advisors, controlling for status effects. Normative homophily is measured by the extent to which two judges are equally “punitive” in awarding damages to plaintiffs. Statistical analyses combine longitudinal advice network data collected among the judges with their normative dispositions. Contrary to what could be expected from conventional sociological theories, we find no pure effect of normative homophily on the choice of advisors. In this case, therefore, sharing the same norms and values does not have, by itself, a mitigating effect and does not contribute to the evolution of the network. We argue that status effects, conformity and alignments on positions of opinion leaders in controversies still provide the best insights into the relationship between norms, structure and behavior.     

当代青年择偶的收入观——郎才女貌悖论

采用2002年中国住户调查数据,本文对当前我国婚姻市场的择偶标准进行了实证研究。在传统社会,高收入的男性和专职于家庭事务的女性被认为是完美的结婚组合(郎才女貌),因此当时的择偶标准体现为收入上的异质性。而本文通过实证研究发现,当前收入水平相当的男女更可能结为夫妻。现在与过去择偶标准的不同导致了郎才女貌悖论的出现。最后,本文阐述了婚介机构的重要性。     

单窃听双跳协作网络的中继选择方案及其性能分析

研究了安全通信意义下,单向译码转发（decode-and-forward,DF）协作无线网络的中继选择问题。针对窃听者既能获得信源发出的信号,又能窃取中继节点转发数据的通信系统,提出了3种中继选择方案来对抗窃听者,增强系统物理层安全性。其中,方案一选择到窃听者信噪比（signal-to-noise ratio,SNR）最小的中继节点;方案二为最大最小（max-min）选择方案,即选择信源到中继节点和中继节点到信宿的较差信噪比中最大值所对应的中继节点;方案三根据窃听信道和主信道的瞬时信道状态信息（channel state information,CSI）选择使得窃听网络有最大保密容量的中继节点。在对各方案的性能分析过程中,得到了各中继选择方案拦截概率的闭式表示,进一步对拦截概率作渐近分析,获得了各中继选择方案的分集阶数。具体地,方案一的分集阶数为1,另外2个中继选择方案的分集阶数均为中继节点个数M。数值结果验证了理论分析得到的结论。