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The Estimation Method of Inference Functions for Margins for Multivariate Models Joe, Harry; Xu, James Jianmeng

Abstract

An estimation approach is proposed for models for a multivariate (non-normal) response with covariates when each of the parameters (either a univariate or a dependence parameter) of the model can be associated with a marginal distribution. The approach consists of estimating univariate parameters from separately maximizing univariate likelihoods, and then estimating dependence parameters from separate bivariate likelihoods or from a multivariate likelihood. The analysis of this method is done through the theory of inference or estimating functions, and the jackknife method is proposed for obtaining standard errors of the parameters and functions of the parameters. The approach proposed here make a large contribution to the computational feasibility of carrying out inference with multivariate models. Examples illustrate the approach, and simulation results are used to indicate the efficiency.

Item Metadata

Title
The Estimation Method of Inference Functions for Margins for Multivariate Models
Creator
Joe, Harry; Xu, James Jianmeng
Date Issued
1996-10
Description
An estimation approach is proposed for models for a multivariate (non-normal) response with covariates when each of the parameters (either a univariate or a dependence parameter) of the model can be associated with a marginal distribution. The approach consists of estimating univariate parameters from separately maximizing univariate likelihoods, and then estimating dependence parameters from separate bivariate likelihoods or from a multivariate likelihood. The analysis of this method is done through the theory of inference or estimating functions, and the jackknife method is proposed for obtaining standard errors of the parameters and functions of the parameters. The approach proposed here make a large contribution to the computational feasibility of carrying out inference with multivariate models. Examples illustrate the approach, and simulation results are used to indicate the efficiency.
Subject
estimating equations; inference functions; copula; jackknife for variance estimation; likelihood; multivariate non-normal response; marginal distributions
Genre
Report
Type
Text
Language
eng
Date Available
2017-01-31
Provider
Vancouver : University of British Columbia Library
Rights
Attribution-NonCommercial-NoDerivs 2.5 Canada
DOI
10.14288/1.0225985
URI
http://hdl.handle.net/2429/57078
Affiliation
Science, Faculty of; Statistics, Department of
Peer Review Status
Unreviewed
Scholarly Level
Faculty
Rights URI
http://creativecommons.org/licenses/by-nc-nd/2.5/ca/
Aggregated Source Repository
DSpace

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Attribution-NonCommercial-NoDerivs 2.5 Canada