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Department seminar by Ejaz Ahmed, Brock UnversityExport this event to calendar

Thursday, October 11, 2012 — 3:00 PM EDT

Relative performance of penalty and shrinkage estimation in weibull censored regression models" 

In this talk we address the problem of estimating the vector of regression parameters in the Weibull censored regression model. Our main objective is to provide natural adaptive estimators that significantly improve upon the classical procedures in the situation where some of the predictors may or may not be associated with the response. In the context of two competing Weibull censored regression models (full model and candidate sub-model), we consider an adaptive shrinkage estimation strategy that shrinks the full model maximum likelihood estimate in the direction of the sub-model estimate. Further, we consider tands for Long and Short Strategic Opportunities (LASSO) strategy and compare the relative performance with the shrinkage estimators. Monte Carlo simulations reveal that when the true model is close to the candidate sub-model, the shrinkage strategy performs better than the LASSO strategy when, and only when, there are many inactive predictors in the model. Shrinkage and LASSO strategies are applied to a real data set from Veteran's administration (VA) lung cancer study to illustrate the usefulness of the procedures in practice.

Location 
M3 - Mathematics 3
3127
200 University Avenue West

Waterloo, ON N2L 3G1
Canada

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