Information matrix equivalence in the presence of censoring: A goodness-of-fit test for semiparametric copula models with multivariate survival data

TL;DR

This paper establishes the information matrix equivalence for semiparametric copula models with censored survival data and introduces an IR goodness-of-fit test based on this principle.

Contribution

It proves the information matrix equivalence under censored data and develops a new IR test for copula model specification in multivariate survival analysis.

Findings

The IR test has accurate size and power in simulations.

The IR test effectively detects misspecified copula models.

Application to real data demonstrates practical utility.

Abstract

Various goodness-of-fit tests are designed based on the so-called information matrix equivalence: if the assumed model is correctly specified, two information matrices that are derived from the likelihood function are equivalent. In the literature, this principle has been established for the likelihood function with fully observed data, but it has not been verified under the likelihood for censored data. In this manuscript, we prove the information matrix equivalence in the framework of semiparametric copula models for multivariate censored survival data. Based on this equivalence, we propose an information ratio (IR) test for the specification of the copula function. The IR statistic is constructed via comparing consistent estimates of the two information matrices. We derive the asymptotic distribution of the IR statistic and propose a parametric bootstrap procedure for the finite-sample $P$-value calculation. The performance of the IR test is investigated via a simulation study and a real data example.