Variable Selection in Semiparametric Quantile Modeling for Longitudinal Data

We propose a penalized quantile regression for partially linear varying coefficient (VC) model with longitudinal data to select relevant non parametric and parametric components simultaneously. Selection consistency and oracle property are established. Furthermore, if linear part and VC part are unknown, we propose a new unified method, which can do three types of selections: separation of varying and constant effects, selection of relevant variables, and it can be carried out conveniently in one step. Consistency in the three types of selections and oracle property in estimation are established as well. Simulation studies and real data analysis also confirm our method.     

Attachment Style,Rules Regarding Sex,and Couple Satisfaction: A Study of Gay Male Couples

This article explores the characteristics of open and closed relationships in 170 gay men according to their levels of satisfaction, their attachment styles, and the rules of having sex outside their relationships for open couples. We found no significant difference between open or closed relationships in levels of satisfaction or attachment styles. However, we found that gay men in open relationships who have explicit rules about having sex with other men are significantly more satisfied that those gay men with no rules. A content analysis of the rules is presented and implications for therapy are drawn from the findings.     

Variable Selection for Kernel Classification

In this article, a variable selection procedure, called surrogate selection, is proposed which can be applied when a support vector machine or kernel Fisher discriminant analysis is used in a binary classification problem. Surrogate selection applies the lasso after substituting the kernel discriminant scores for the binary group labels, as well as values for the input variable observations. Empirical results are reported, showing that surrogate selection performs well.     

Application of a novel PROMETHEE-based method for construction of a group compromise ranking to prioritization of green suppliers in food supply chain

The food sector has a prodigious focus and is constantly gaining in importance in today’s global economic marketplace. Due to an increasing global population, society faces a greater challenge for sustainable food production, quality, distribution, and food safety in the food supply chain. Adopting green supply chain management (GSCM) elements is essential for utilizing the food supply chain in an environmentally benign way. As a solution to the above challenge, the economic and green characteristics for supplier selection in green purchasing are studied in this paper. For an organization, the evaluation and selection of the green supplier is a vital issue due to several tangible and intangible criteria involved. Accordingly, we apply multiple criteria decision aiding techniques.We propose a hybrid approach that combines the revised Simos procedure, PROMETHEE methods, algorithms for constructing a group compromise ranking, and robustness analysis. At first, the revised Simos procedure is used to derive the criteria weights. Next, the PROMETHEE method is applied to rank the suppliers according to each Decision Maker׳s (DM׳s) preferences. Then, the compromise ranking is constructed to minimize the distance of the individual׳s rankings from the solution adopted by the whole group. For this purpose, we introduce and apply some original procedures based on Binary Linear Programming. Finally, the results are validated against the outcomes of robustness analysis. The applicability and efficiency of the proposed approach is endorsed with a case study in an Indian food industry.     

Information theoretic criterion approach to dimensionality reduction in multinomial logistic regression models. part i: theory

We discuss the issue of dimensionality reduction in multinomial logistic models as problems arising in variable selection, collapsibility of responses and linear restrictions in the parameter matrix. A method using the information theoretic criterion suggested by Bai, Krishnaiah and Zhao, a variation of Akaike information criterion, is used to estimate the rank of the parameter matrix. The same procedure is used for the selection of variables and collapsibility of response categories. The strong consistency of this procedure is established in all the problems.     

Selecting the best predictor variate

The Bechhofer indifference-zone approach is used to determine the sample size for selecting the best predictor variate from a set of k variates. A multivariate normal model is assumed and the best predictor variate is defined to be that variate for which the predictand has the smallest population conditional variance. Asymptotic distribution theory and probability bounds are employed to obtain sample-size approximations, which are compared with (numerical) exact results.     

The Shifting Sands of Open Access Publishing,a Publisher's View

Abstract

This paper sets Open Access (OA) publishing in the context of today's scientific, technical, and medical (STM) publishing trends. Four areas are covered: (a) a brief overview of STM publishing and its value today; (b) OA's place in the industry; (c) the underlying economics of OA, particularly its author-pays model; and (d) directions in moving towards “universal access” to STM information, where both researchers and the public have access to the scientific information they need.     

A monte carlo study of variable selection and inferences in a two stage random coefficient linear regression model with unbalanced data

A growth curve analysis is often applied to estimate patterns of changes in a given characteristic of different individuals. It is also used to find out if the variations in the growth rates among individuals are due to effects of certain covariates. In this paper, a random coefficient linear regression model, as a special case of the growth curve analysis, is generalized to accommodate the situation where the set of influential covariates is not known a priori. Two different approaches for seleaing influential covariates (a weighted stepwise selection procedure and a modified version of Rao and Wu’s selection criterion) for the random slope coefficient of a linear regression model with unbalanced data are proposed. Performances of these methods are evaluated by means of Monte-Carlo simulation. In addition, several methods (Maximum Likelihood, Restricted Maximum Likelihood, Pseudo Maximum Likelihood and Method of Moments) for estimating the parameters of the selected model are compared Proposed variable selection schemes and estimators are appliedtotheactualindustrial problem which motivated this investigation.     

Robust variable selection in finite mixture of regression models using the t distribution

Abstract

Variable selection in finite mixture of regression (FMR) models is frequently used in statistical modeling. The majority of applications of variable selection in FMR models use a normal distribution for regression error. Such assumptions are unsuitable for a set of data containing a group or groups of observations with heavy tails and outliers. In this paper, we introduce a robust variable selection procedure for FMR models using the t distribution. With appropriate selection of the tuning parameters, the consistency and the oracle property of the regularized estimators are established. To estimate the parameters of the model, we develop an EM algorithm for numerical computations and a method for selecting tuning parameters adaptively. The parameter estimation performance of the proposed model is evaluated through simulation studies. The application of the proposed model is illustrated by analyzing a real data set.     

Stopping guidelines vs stopping rules: a practitioner's point of view

Monitoring interim accumulating data in a clinical trial for evidence of therapeutic benefit or toxicity is a frequent policy, usually carried out by an independent scientific committee. While statistical methodology has been developed to assess the significance of these interim analyses, such methods should not be viewed as absolute rules but only serve as useful guides. The decision process to terminate a trial early is very complex and many factors must be taken into account. The complexity of this decision process is illustrated by reviewing the experience of several recent clinical trials.