We consider the problem of accounting for model uncertainty in linear regression models.
Conditioning on a single selected model ignores model uncertainty, and thus leads to the …

We consider the problems of variable selection and accounting for model uncertainty in
linear regression models. Conditioning on a single selected model ignores model …

This article is concerned with the selection of subsets of predictor variables in a linear
regression model for the prediction of a dependent variable. It is based on a Bayesian …

When a number of distinct models contend for use in prediction, the choice of a single model
can offer rather unstable predictions. In regression, stochastic search variable selection with …

The mfp package is targeted at the use of multivariable fractional polynomials for modelling
the influence of continuous, categorical and binary covariates on the outcome in regression …

In regression models, such as generalized linear models, there is often substantial prior
uncertainty about the choice of covariates to include. Conceptually, the Bayesian paradigm …

Standard statistical practice ignores model uncertainty. Data analysts typically select a
model from some class of models and then proceed as if the selected model had generated …

Linear regression analyses commonly involve two consecutive stages of statistical inquiry. In
the first stage, a single 'best'model is defined by a specific selection of relevant predictors; in …

This article describes the BMS (Bayesian model sampling) package for R that implements
Bayesian model averaging for linear regression models. The package excels in allowing for …

In situations where limited knowledge of a system exists and the ratio of data points to
variables is small, variable selection methods can often be misleading. Freedman (Am Stat …