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Department Colloquium

Speaker: Brian Ling (³ÉÈË´óƬ)

Title: Shape-Constrained Estimation with Incomplete Data

Abstract:
Many problems in statistics involve incomplete or indirect observation of the variable of interest, such as interval-censored data and current duration data. In these settings, likelihood-based estimation is inherently a shape-constrained problem, since the parameter of interest is a distribution or survival function.

In this talk, I will survey several shape-constrained estimation problems and those arising from incomplete-data models. I will then focus on recent work on current duration data and interval-censored data under log-concavity, where additional structural constraints enable faster convergence in a fully nonparametric, tuning-parameter-free framework. I will discuss theoretical properties such as rates of convergence, as well as the associated computational algorithms.