Regression Modelling using I-Priors


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.

19 May 2015 12:00 pm — 12:35 pm
LSE Department of Statistics PhD Presentation Event
LSE, London, United Kingdom
Haziq Jamil
Haziq Jamil
Assistant Professor in Statistics

My research interests include statistical theory, methods and computation, with applications towards the social sciences.