A0496
Title: Geometrically weighted compositional data analysis for forecasting life-table death counts
Authors: Han Lin Shang - Macquarie University (Australia) [presenting]
Abstract: Age-specific life-table death counts observed over time are examples of densities. Non-negativity and summability are two constraints that prevent the direct implementation of standard statistical methods. Compositional data analysis presents a one-to-one mapping from constrained to unconstrained space to rectify the constraints. We introduce a weighted compositional data analysis for modeling and forecasting life-table death counts. Our proposed method assigns higher weights to more recent data and provides a modeling scheme that is easily adapted to allow for constraints. We illustrate our method using Swedish age-specific life-table death counts from 1751 to 2020 and show that the weighted compositional data analytic method improves forecast accuracy compared to their unweighted counterparts.
