Copula Functions for Spatial Survival Data Analysis

Document Type : Original Research Articles

Authors
Mohsen Mohammadzadeh 1
Nasrin Ebrahimi 1
Giuliana Cortese 2
1 1 Department of Statistics‎, ‎Tarbiat Modares University‎, ‎Tehran‎, ‎Islamic Republic of Iran
2 2 Department of Statistical Sciences‎, ‎Padua University‎, ‎Italy
10.22059/jsciences.2023.350366.1007763
Abstract
Many survival data analyses aim to assess the effect of different risk factors on survival time‎. ‎In some studies‎, ‎the survival times are correlated‎, ‎and the dependence between survival times is related to their spatial locations‎. ‎Identifying and considering the dependence structure of data is essential in survival modeling‎. ‎The copula functions are helpful tools for incorporating data dependencies‎. ‎So‎, ‎one may use these functions for modelling spatial survival data‎. ‎This paper presents a model for spatial survival data by the Gumbel-Hougaard copula function‎. ‎A two-stage estimator using a composite likelihood function is used to estimate regression and dependence parameters‎. ‎A simulation study investigates the performance of the model‎. ‎Finally‎, ‎the proposed model is applied to model a set of COVID-19 data.
Keywords
Spatial Survival Data
Copula Function
Composite Likelihood
Frailty Models
Two-Stage Estimator
Subjects
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Journal of Sciences, Islamic Republic of Iran
Volume 34, Issue 1
Winter 2023
Pages 35-42