A Loss-Based Prior for Variable Selection in Linear Regression Methods
نویسندگان
چکیده
منابع مشابه
Optimal prior-free probabilistic variable selection in Gaussian linear regression
Model selection is fundamental to scientific discovery, but a general framework that gives valid prior-free probabilistic assessments of the quality of individual models remains to be worked out. In this paper, we propose an inferential model (IM) approach to accomplish this for the special case of variable selection in a full-rank Gaussian linear regression model. This proposal is based on the...
متن کاملVariable selection in linear regression through adaptive penalty selection
Model selection procedures often use a fixed penalty, such as Mallows’ Cp, to avoid choosing a model which fits a particular data set extremely well. These procedures are often devised to give an unbiased risk estimate when a particular chosen model is used to predict future responses. As a correction for not including the variability induced in model selection, generalized degrees of freedom i...
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 کاملBayesian linear regression and variable selection for spectroscopic calibration.
This paper presents a Bayesian approach to the development of spectroscopic calibration models. By formulating the linear regression in a probabilistic framework, a Bayesian linear regression model is derived, and a specific optimization method, i.e. Bayesian evidence approximation, is utilized to estimate the model "hyper-parameters". The relation of the proposed approach to the calibration mo...
متن کاملRobust variable selection for mixture linear regression models
In this paper, we propose a robust variable selection to estimate and select relevant covariates for the finite mixture of linear regression models by assuming that the error terms follow a Laplace distribution to the data after trimming the high leverage points. We introduce a revised Expectation-maximization (EM) algorithm for numerical computation. Simulation studies indicate that the propos...
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: Bayesian Analysis
سال: 2020
ISSN: 1936-0975
DOI: 10.1214/19-ba1162