Date: 2012-03-16

Time: 15:30-16:30

Location: BURN 1205

Abstract:

Follow-up studies are frequently carried out to investigate the evolution of measurements through time, taken on a set of subjects. These measurements (responses) are bound to be influenced by subject specific covariates and if a regression model is used the data analyst is faced with the problem of selecting those covariates that “best explain” the data. For example, in a clinical trial, subjects may be monitored for a response following the administration of a treatment with a view of selecting the covariates that are best predictive of a treatment response. This variable selection setting is standard. However, more realistically, there will often be an unknown delay from the administration of a treatment before it has a measurable effect. This delay will not be directly observable since it is a property of the distribution of responses rather than of any particular trajectory of responses. Briefly, each subject will have an unobservable change-point. With a change-point component added, the variable selection problem necessitates the use of penalized likelihood methods. This is because the number of putative covariates for the responses, as well as the change-point distribution, could be large relative to the follow-up time and/or the number of subjects; variable selection in a change-point setting does not appear to have been studied in the literature. In this talk I will briefly introduce the multi-path change-point problem. I will show how variable selection for the covariates before the change, after the change, as well as for the change-point distribution, reduces to variable selection for a finite mixture of multivariate distributions. I will discuss the performance of my model selection methods using an example on cognitive decline in subjects with Alzheimer’s disease and through simulations.

Speaker

Azadeh Shohoudi is a PhD student in our department, under the supervision of David Wolfson. The work she will present was done jointly with David Wolfson, Masoud Asgharian and Abbas Khalili.