In statistical modelling, there is often a genuine interest to learn the most reasonable, parsimonious, and interpretable model that fits the data. We turn our attention to the problem of variable selection in the context of ordinary linear …

We introduce a methodology with the aim of providing a unifying framework for esti- mating a variety of regression methods and models, including multilevel, varying coefficient, longitudinal models, and models with functional covariates and …

Regression modelling using priors with Fisher information covariance kernels (I-priors).

Regression analysis is undoubtedly an important tool to understand the relationship between one or more explanatory and independent variables of interest. In this thesis, we explore a novel methodology for fitting a wide range of parametric and …

In a regression setting, we define an I-prior as a Gaussian process prior on the regression function with covariance kernel equal to its Fisher information. We present some methodology and computational work on estimating regression functions by …

This is an overview of the R package iprior, which implements a unified methodology for fitting parametric and nonparametric regression models, including additive models, multilevel models, and models with one or more functional covariates. Based on …

This is an overview of a unified methodology for fitting parametric and nonparametric regression models, including additive models, multilevel models, and models with one or more functional covariates. We also discuss an associated R-package called …

An extension of the I-prior methodology to binary response data is explored. Starting from a latent variable approach, it is assumed that there exists continuous, auxiliary random variables which decide the outcome of the binary responses. Fitting a …

Bayesian Variable Selection for Linear Models using I-priors in `R`.

An `R` package for I-prior regression.

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