Open Collections

UBC Faculty Research and Publications

Likelihood Inference for Factor Copula Models with Asymmetric Tail Dependence Joe, Harry; Li, Xiaoting

Abstract

For multivariate non-Gaussian involving copulas, likelihood inference is dominated by the data in the middle, and fitted models might not be very good for joint tail inference, such as assessing the strength of tail dependence. When preliminary data and likelihood analysis suggest asymmetric tail dependence, a method is proposed to improve extreme value inferences based on the joint lower and upper tails. A prior that uses previous information on tail dependence can be used in combination with the likelihood. With the combination of the prior and the likelihood (which in practice has some degree of misspecification) to obtain a tilted log-likelihood, inferences with suitably transformed parameters can be based on Bayesian computing methods or with numerical optimization of the tilted log-likelihood to obtain the posterior mode and Hessian at this mode.

Item Metadata

Title
Likelihood Inference for Factor Copula Models with Asymmetric Tail Dependence
Creator
Joe, Harry; Li, Xiaoting
Publisher
Multidisciplinary Digital Publishing Institute
Date Issued
2024-07-19
Description
For multivariate non-Gaussian involving copulas, likelihood inference is dominated by the data in the middle, and fitted models might not be very good for joint tail inference, such as assessing the strength of tail dependence. When preliminary data and likelihood analysis suggest asymmetric tail dependence, a method is proposed to improve extreme value inferences based on the joint lower and upper tails. A prior that uses previous information on tail dependence can be used in combination with the likelihood. With the combination of the prior and the likelihood (which in practice has some degree of misspecification) to obtain a tilted log-likelihood, inferences with suitably transformed parameters can be based on Bayesian computing methods or with numerical optimization of the tilted log-likelihood to obtain the posterior mode and Hessian at this mode.
Subject
copula; latent variable; likelihood; prior; Bayesian computing; numerical optimization
Genre
Article
Type
Text
Language
eng
Date Available
2024-08-07
Provider
Vancouver : University of British Columbia Library
Rights
CC BY 4.0
DOI
10.14288/1.0445015
URI
http://hdl.handle.net/2429/88903
Affiliation
Science, Faculty of; Statistics, Department of
Citation
Entropy 26 (7): 610 (2024)
Publisher DOI
10.3390/e26070610
Peer Review Status
Reviewed
Scholarly Level
Faculty
Rights URI
https://creativecommons.org/licenses/by/4.0/
Aggregated Source Repository
DSpace

Item Media

Item Citations and Data

Rights

CC BY 4.0