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Inference Procedures for Copula-Based Models of Bivariate Dependence

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Inference Procedures for Copula-Based Models of Bivariate Dependence

LOUDEGUI DJIMDOU, Magloire (2021) Inference Procedures for Copula-Based Models of Bivariate Dependence. PhD thesis, Concordia University.

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Abstract

In this thesis, we develop inference procedures for copula-based models of bivariate dependence. We first investigate the distribution of Kendall’s functions for joint survivors since Kendall’s functions
are important for identifying Archimedean copula models. We then provide two estimators for the generator of an Archimedean copula. We also propose a plug-in estimator for Kendall’s tau and a
maximum pseudo-likelihood estimation for the copula model parameters in cases where the survival times are subject to bivariate random censoring. These tests are based on the usual (univariate) Kaplan-Meier estimator and a recently proposed estimator for the bivariate case.

Divisions:Concordia University > Faculty of Arts and Science > Mathematics and Statistics
Item Type:Thesis (PhD)
Authors:LOUDEGUI DJIMDOU, Magloire
Institution:Concordia University
Degree Name:Ph. D.
Program:Mathematics
Date:12 November 2021
Thesis Supervisor(s):Sen, Arusharka and Chaubey, Yogendra
Keywords:Archimedean copula, Kendall distribution, generator, distribution, estimator, asymptotic variance, influence function
ID Code:990063
Deposited By: Magloire LOUDEGUI DJIMDOU
Deposited On:16 Jun 2022 15:18
Last Modified:16 Jun 2022 15:18
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