We are glad to announce the following Statistics Seminar: 

Friday, October 28, 11.30 a.m. 

Davide Ferrari

(Free University of Bozen-Bolzano)

Fast Construction of Optimal Composite Likelihoods

Abstract

A composite likelihood is a combination of low-dimensional likelihood objects useful in applications where the data have complex structure. Although composite likelihood construction is a crucial aspect influencing both computing and statistical properties of the resulting estimator, currently there does not seem to exist a universal rule to combine low-dimensional likelihood objects that is statistically justified and fast in execution. This paper develops a methodology to select and combine the most informative low-dimensional likelihoods from a large set of candidates while carrying out parameter estimation. The new procedure minimizes the distance between composite likelihood and full likelihood scores subject to a constraint representing the afforded computing cost. The selected composite likelihood is sparse in the sense that it contains a relatively small number of informative sub-likelihoods while the noisy terms are dropped. The resulting estimator is found to have asymptotic variance close to that of the minimum-variance estimator constructed using all the low-dimensional likelihoods.

Preprint on Statistica Sinica https://www3.stat.sinica.edu.tw/ss_newpaper/SS-2021-0235_na.pdf

Room 2, Piazza Scaravilli 2, Bologna

Link for attending the event:  Fai clic qui per partecipare alla riunione

Contact person: Simone Giannerini