Random cohort effects and age groups dependency structure for mortality modelling and forecasting: Mixed-effects time-series model approach

• URL: http://arxiv.org/abs/2112.15258v1
• Date: Fri, 31 Dec 2021 01:15:07 GMT
• Title: Random cohort effects and age groups dependency structure for mortality modelling and forecasting: Mixed-effects time-series model approach
• Authors: Ka Kin Lam, Bo Wang
• Abstract summary: The article proposes a novel mixed-effects time-series approach for mortality modelling and forecasting. The abilities of the proposed approach are demonstrated through two applications with empirical male and female mortality data.
• Score: 3.450774887322348
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There have been significant efforts devoted to solving the longevity risk given that a continuous growth in population ageing has become a severe issue for many developed countries over the past few decades. The Cairns-Blake-Dowd (CBD) model, which incorporates cohort effects parameters in its parsimonious design, is one of the most well-known approaches for mortality modelling at higher ages and longevity risk. This article proposes a novel mixed-effects time-series approach for mortality modelling and forecasting with considerations of age groups dependence and random cohort effects parameters. The proposed model can disclose more mortality data information and provide a natural quantification of the model parameters uncertainties with no pre-specified constraint required for estimating the cohort effects parameters. The abilities of the proposed approach are demonstrated through two applications with empirical male and female mortality data. The proposed approach shows remarkable improvements in terms of forecast accuracy compared to the CBD model in the short-, mid-and long-term forecasting using mortality data of several developed countries in the numerical examples.

Related papers

• A comparison of short-term probabilistic forecasts for the incidence of COVID-19 using mechanistic and statistical time series models [15.031837435365532]
  Short-term forecasts of infectious disease spread are a critical component in risk evaluation and public health decision making. We compare short-term probabilistic forecasts of popular mechanistic models with forecasts of statistical time series models. We find that, on average, probabilistic forecasts from statistical time series models are overall at least as accurate as forecasts from mechanistic models.
  arXiv  Detail & Related papers  (2023-05-01T16:37:43Z)
• Development of deep biological ages aware of morbidity and mortality based on unsupervised and semi-supervised deep learning approaches [3.8240573706733514]
  Existing deep learning methods for biological age estimation usually depend on chronological ages and lack of consideration of mortality and morbidity. This paper proposes a novel deep learning model to learn latent representations of biological aging in regard to subjects' morbidity and mortality.
  arXiv  Detail & Related papers  (2023-02-01T08:54:00Z)
• SurvLatent ODE : A Neural ODE based time-to-event model with competing risks for longitudinal data improves cancer-associated Deep Vein Thrombosis (DVT) prediction [68.8204255655161]
  We propose a generative time-to-event model, SurvLatent ODE, which parameterizes a latent representation under irregularly sampled data. Our model then utilizes the latent representation to flexibly estimate survival times for multiple competing events without specifying shapes of event-specific hazard function. SurvLatent ODE outperforms the current clinical standard Khorana Risk scores for stratifying DVT risk groups.
  arXiv  Detail & Related papers  (2022-04-20T17:28:08Z)
• When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting [70.54920804222031]
  Most existing forecasting models disregard uncertainty quantification, resulting in mis-calibrated predictions. Recent works in deep neural models for uncertainty-aware time-series forecasting also have several limitations. We model the forecasting task as a probabilistic generative process and propose a functional neural process model called EPIFNP.
  arXiv  Detail & Related papers  (2021-06-07T18:31:47Z)
• Robust non-parametric mortality and fertility modelling and forecasting: Gaussian process regression approaches [3.450774887322348]
  A precise model for forecasting demographic movements is important for decision making in social welfare policies. This article introduces a novel non-parametric approach using Gaussian process regression with a natural cubic spline mean function.
  arXiv  Detail & Related papers  (2021-02-18T23:49:25Z)
• Multipopulation mortality modelling and forecasting: The multivariate functional principal component with time weightings approaches [3.450774887322348]
  We introduce two new models for joint mortality modelling and forecasting multiple subpopulations. The first proposed model extends the independent functional data model to a multi-population modelling setting. The second proposed model outperforms the first model as well as the current models in terms of forecast accuracy.
  arXiv  Detail & Related papers  (2021-02-18T21:01:58Z)
• Bayesian prognostic covariate adjustment [59.75318183140857]
  Historical data about disease outcomes can be integrated into the analysis of clinical trials in many ways. We build on existing literature that uses prognostic scores from a predictive model to increase the efficiency of treatment effect estimates.
  arXiv  Detail & Related papers  (2020-12-24T05:19:03Z)
• STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization [76.57716281104938]
  We develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic.
  arXiv  Detail & Related papers  (2020-12-08T21:21:47Z)
• An Optimal Control Approach to Learning in SIDARTHE Epidemic model [67.22168759751541]
  We propose a general approach for learning time-variant parameters of dynamic compartmental models from epidemic data. We forecast the epidemic evolution in Italy and France.
  arXiv  Detail & Related papers  (2020-10-28T10:58:59Z)
• A General Framework for Survival Analysis and Multi-State Modelling [70.31153478610229]
  We use neural ordinary differential equations as a flexible and general method for estimating multi-state survival models. We show that our model exhibits state-of-the-art performance on popular survival data sets and demonstrate its efficacy in a multi-state setting.
  arXiv  Detail & Related papers  (2020-06-08T19:24:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.