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Constructing prediction intervals for the age distribution of deaths

Shang, H. & Haberman, S. ORCID: 0000-0003-2269-9759 (2025). Constructing prediction intervals for the age distribution of deaths. Scandinavian Actuarial Journal, pp. 1-18. doi: 10.1080/03461238.2025.2544265

Abstract

We introduce a model-agnostic procedure to construct prediction intervals for the age distribution of deaths. The age distribution of deaths is an example of constrained data, which are nonnegative and have a constrained integral. A centered log-ratio transformation and a cumulative distribution function transformation are used to remove the two constraints, where the latter transformation can also handle the presence of zero counts. Our general procedure divides data samples into training, validation, and testing sets. Within the validation set, we can select an optimal tuning parameter by calibrating the empirical coverage probabilities to be close to their nominal ones. With the selected optimal tuning parameter, we then construct the pointwise prediction intervals using the same models for the holdout data in the testing set. Using Japanese age- and sex-specific life-table death counts, we assess and evaluate the interval forecast accuracy with a suite of functional time-series models.

Publication Type: Article
Additional Information: © 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
Publisher Keywords: compositional data analysis, functional principal component analysis, functional time series, prediction interval calibration, split conformal prediction, standard deviation-based conformity
Subjects: H Social Sciences > HA Statistics
Departments: Bayes Business School
Bayes Business School > Faculty of Actuarial Science & Insurance
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