Asymptotic properties of robust regression estimators are well known. However, it is not always clear what the best strategy for confidence intervals and hypothesis testing when sample size very large, since distribution residuals coming from estimates has unknown small samples. In present work we propose an analysis various strategies estimating variance-covariance matrix S at variation n p, c...

in most of the multi–criteria decision–analysis (mcda) problems in which the choquet integral is used as aggregation function, the coefficients of choquet integral (capacity) are not known in advance. actually, they could be calculated by capacity definition methods. in these methods, the preference information of decision maker (dm) is used to constitute a possible solution space. the methods ...

In this paper, a new approach is presented to fit arobust fuzzy regression model based on some fuzzy quantities. Inthis approach, we first introduce a new distance between two fuzzynumbers using the kernel function, and then, based on the leastsquares method, the parameters of fuzzy regression model isestimated. The proposed approach has a suitable performance to<b...

In this paper, the optimizations problems to seek robust solutions under uncertainty are considered. The light robust approach is one of the strong and new methods to achieve robust solutions under conditions of uncertainty. In this paper, we tried to improve the quality of the solutions obtained from the Light Robust method by introducing a revised approach. Considering the problem concerned, ...

Many regression problems exhibit a natural grouping among predictor variables. Examples are groups of dummy variables representing categorical variables, or present and lagged values of time series data. Since model selection in such cases typically aims for selecting groups of variables rather than individual covariates, an extension of the popular least angle regression (LARS) procedure to gr...

In robust nonparametric kernel regression context,weprescribemethod to select trimming parameter and bandwidth. Through solving estimating equations, we control outlier effect through combining weighting and trimming. We show asymptotic consistency, establish bias, variance properties and derive asymptotics. © 2016 Elsevier B.V. All rights reserved.

Background: Regarding the increased risk of developing type 2 diabetes in pre-diabetic people, identifying pre-diabetes and determining of its risk factors seems so necessary. In this study, it is aimed to compare ordinary logistic regression and robust logistic regression models in modeling pre-diabetes risk factors. Methods: This is a cross-sectional study and conducted on 6460 people, over ...