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Activity Number: 316 - Emerging Advances of Innovative Computational Skills with Unconventional Likelihoods
Type: Invited
Date/Time: Tuesday, July 30, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Computing
Abstract #300009 Presentation
Title: A Broad Framework for Likelihood Alternatives in View of Small, Very Large, and Variable-Size Data
Author(s): Geert Molenberghs*
Companies: Universiteit Hasselt & Katholieke Universiteit Leuven
Keywords: likelihood; computatoin; pseudo-likelihood; split sample; pairwise likelihood

We consider a number of data settings where the use of standard maximum likelihood or other estimation method is complicated for a number of reasons: data structures are complex, there are very large data streams, in reverse there are very small trials (like in orphan diseases), or there are non-standard design features (sequential trials, missing data, clustered data with variable size, etc.).

The use of alternatives to maximum likelihood are explored, with particular emphasis on pseudo-likelihood, split-sample methods, and even closed-form estimators in settings where one would not expect them.

Specific attention is devoted to the computational feasibility of the proposed methods. We pay particular attention to the existence of closed forms in our modified procedures.

All settings are illustrated using real-life examples.

Authors who are presenting talks have a * after their name.

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