Regression Modelling using I-Priors

Abstract

The I-prior methodology is a new modelling technique which aims to improve on maximum likelihood estimation of linear models when the dimensionality is large relative to the sample size. By putting a prior which is informed by the dataset (as opposed to a subjective prior), advantages such as model parsimony, lesser model assumptions, simpler estimation, and simpler hypothesis testing can be had. By way of introducing the I-prior methodology, we will give examples of linear models estimated using I-priors. This includes multiple regression models, smoothing models, random effects models, and longitudinal models. Research into this area involve extending the I-prior methodology to generalised linear models (e.g. logistic regression), Structural Equation Models (SEM), and models with structured error covariances.

Date
Event
LSE Department of Statistics PhD Presentation Event
Location
LSE, London, United Kingdom