A Khmaladze-Transformed Test of Fit with ML Estimation in the Presence of Recurrent Events

Abstract

This article provides a goodness-of-fit test for the distribution function or the survival function in a recurrent event setting, when the inter-event time parametric structure F( · ; θ) is estimated from the observed data. Of concern is the null hypothesis that the inter-event time distribution is absolutely continuous and belongs to a parametric family ℱ = {F(· ; θ) : θ ∈ Θ ⊆ ℜq} where the q-dimensional parameter space is neither known nor specified. We proposed a Khmaladze martingale-transformed type of test (Khmaladze, 1981), adapted to recurrent events. The test statistic combines two likelihood sources of estimation to form a parametric empirical process: (1) a product-limit nonparametric maximum likelihood estimator (NPMLE; Pena et al., 2001a) that is a consistent estimator of F, [F hat] say, and (2) a point process likelihood estimator F( · ;[θ hat] ) (Jacod, 1974/1975). These estimators are combined to construct a Kolmogorov-Smirnov (KS) type of test (Kolmogorov 1933; Smirnov, 1933). Empirical process and martingale weak convergence frameworks are utilized for theoretical derivations and motivational justification of the proposed transformation. A simulation study is conducted for performance assessment, and the test is applied to a problem investigated by Proschan (1963) on air-conditioning failure in a fleet of Boeing 720 jets.

Department(s)

Mathematics and Statistics

Keywords and Phrases

Empirical Process; Finite Elements Discretization; Khmaladze Transform; KS Test; Martingales; Recurrent Event

International Standard Serial Number (ISSN)

0747-4946; 1532-4176

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2019 Taylor & Francis Inc., All rights reserved.

Publication Date

01 Jul 2019

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