Consistent variable selection for functional regression models
نویسندگان
چکیده
منابع مشابه
Variable Selection for Regression Models
A simple method for subset selection of independent variables in regression models is proposed. We expand the usual regression equation to an equation that incorporates all possible subsets of predictors by adding indicator variables as parameters. The vector of indicator variables dictates which predictors to include. Several choices of priors can be employed for the unknown regression coeecie...
متن کاملVariable Selection for Multivariate Logistic Regression Models
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...
متن کاملAlternative Strategies for Variable Selection in Linear Regression Models
1. INTRODUCTION 1.1.1. Variable Selection for Incomplete Data sets In statistical practice, many real-life data sets are incomplete for reasons like non-responses or drop-outs. When a data set is incomplete, practitioners frequently resort to a " case-deletion " strategy within which the incomplete cases are excluded from analysis and the complete cases are formed into a reduced rectangular com...
متن کاملVariable Selection for Regression Models with Missing Data.
We consider the variable selection problem for a class of statistical models with missing data, including missing covariate and/or response data. We investigate the smoothly clipped absolute deviation penalty (SCAD) and adaptive LASSO and propose a unified model selection and estimation procedure for use in the presence of missing data. We develop a computationally attractive algorithm for simu...
متن کاملFWDselect: An R Package for Variable Selection in Regression Models
In multiple regression models, when there are a large number (p) of explanatory variables which may or may not be relevant for predicting the response, it is useful to be able to reduce the model. To this end, it is necessary to determine the best subset of q (q ≤ p) predictors which will establish the model with the best prediction capacity. FWDselect package introduces a new forward stepwiseb...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Journal of Multivariate Analysis
سال: 2016
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2015.06.007