Jazi Omidali Aghababaei
Title: MAPLE; Semiparametric Estimation and Variable Selection for Length-biased Data with Heavy Censoring.
Abstract:
In this talk, we discuss two problems of semiparametric estimation and variable selection for length-biased data with heavy censoring. The common feature of the proposed estimation procedures in the literature is that they only put probability mass on failure times. Under length-biased sampling, however, censoring is informative and failing to incorporate censored observations into estimation can lead to a substantial loss of efficiency. We propose two estimation procedures by computing the likelihood contribution of both uncensored and censored observations. For variable selection problem, we introduce a unified penalized estimating function and use an optimization algorithm to solve it. We discuss the asymptotic properties of the resulting penalized estimators. The work is motivated by the International stroke Trial dataset collected in Argentina in which the survival times of about 88% of the 545 cases are censored.
Speaker
Omid Aghababaei is a Postdoctoral Fellow at The Hospital for Sick Children, Peter Gilgan Centre for Research & Learning, Child Health Evaluative Sciences. His supervisors are Professor Masoud Asgharian and Prof. Abbas Khalili