May 2021 Mortality Modeling Under Stochastic Frailty
Kazi Tanvir Hasan, Olcay Akman
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Missouri J. Math. Sci. 33(1): 105-118 (May 2021). DOI: 10.35834/2021/3301105

Abstract

When mortality statistics are reported for fatal diseases, they reflect the ratio of the overall mortality within the target population which is impacted from the disease in question. Reporting overall mortality leads to erroneous predictions since cohorts with different frailties are impacted at different rates. In this paper, we study methods for predicting mortality under varying conditions with the goal of removing the impact of hidden heterogeneity from the parameter estimates.

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Kazi Tanvir Hasan. Olcay Akman. "Mortality Modeling Under Stochastic Frailty." Missouri J. Math. Sci. 33 (1) 105 - 118, May 2021. https://doi.org/10.35834/2021/3301105

Information

Published: May 2021
First available in Project Euclid: 4 June 2021

Digital Object Identifier: 10.35834/2021/3301105

Subjects:
Primary: 62F15
Secondary: 62N99

Keywords: Bayes factor , frailty , inverse Gaussian distribution , Lifetime Modeling , Model selection , weighted distribution

Rights: Copyright © 2021 University of Central Missouri, School of Computer Science and Mathematics

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Vol.33 • No. 1 • May 2021
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