In this paper, we proposed an adaptive robust variable selection procedure for the logistic regression model. The method is to outliers and considers goodness-of-fit of Furthermore, apply MM algorithm solve optimization problem. Monte Carlo studies are evaluated finite-sample performance method. results show that when there in dataset or distribution covariate deviates from normal distribution,...

In this paper we intend to improve the explanatory power of regressions when the deletion method is used for the remedy of Multicolinearity. If one deletes the variable (s) that is (are) responsible for Multicolinearity, he loses some information that is not common between the deleted variable (s) and the other remaining variables in the regression. To improve this method, we run the deleted va...

where φ is the parameter of interest which models the relationship while U is an error term. Contrary to usual statistical regression models, the error term is correlated with the explanatory variables X, hence E(U |X) 6= 0, preventing direct estimation of φ. To overcome the endogeneity of X, we assume that there exists an observed random variable W , called the instrument, which decorrelates t...

In this paper, we are concerned with how to select significant variables in semiparametric modeling. Variable selection for semiparametric regression models consists of two components: model selection for nonparametric components and selection of significant variables for the parametric portion. Thus, semiparametric variable selection is much more challenging than parametric variable selection ...

In this paper, we use multivariate logistic regression models to incorporate correlation among binary response data. Our objective is to develop a variable subset selection procedure to identify important covariates in predicting correlated binary responses using a Bayesian approach. In order to incorporate available prior information, we propose a class of informative prior distributions on th...

Nonlinear kernel regression models are often used in statistics and machine learning due to greater accuracy than linear models. Variable selection for kernel regression models is a challenge partly because, unlike the linear regression setting, there is no clear concept of an effect size for regression coefficients. In this paper, we propose a novel framework that provides an analog of the eff...

This paper estimates an additive model semiparametrically, while automatically selecting the significant independent variables and the app~opriatc power transformation of the dependent variable. The nonlinear variables arc modeled as regression splincs, with significant knots selected fiom a large number of candidate knots. The estimation is made robust by modeling the errors as a mixture of no...

In fuzzy domain, a variable (vague linguistic term) often depends not only on a single variable but on more then one variables. In such a situation multiple regression analysis is more appropriate than simple regression analysis involving one independent variable. This paper introduces fuzzy multiple regression equations of fuzzy sets those are treated as a variable with certain values assigned...

Process Modeling by Bayesian Latent Variable Regression Mohamed N. Nounou, Bhavik R. Bakshi Prem K. Goel, Xiaotong Shen Department of Chemical Engineering Department of Statistics The Ohio State University, Columbus, OH 43210, USA Abstract Large quantities of measured data are being routinely collected in a variety of industries and used for extracting linear models for tasks such as, process c...