Literature DB >> 29592794

A mortality model based on a mixture distribution function.

Stefano Mazzuco1, Bruno Scarpa1, Lucia Zanotto1.   

Abstract

A new mortality model based on a mixture distribution function is proposed. We mix a half-normal distribution with a generalization of the skew-normal distribution. As a result, we get a six-parameter distribution function that has a good fit with a wide variety of mortality patterns. This mixture model is fitted to several mortality data schedules and compared with the Siler (five-parameter) and Heligman-Pollard (eight-parameter) models. Our proposal serves as a convenient compromise between the Heligman-Pollard model (which ensures a good fit with data but is often overparameterized) and the Siler model (which is more compact but fails to capture 'accident humps').

Keywords:  accident hump; half-normal distribution; mixture distribution; mortality model; skew-bimodal-normal distribution

Mesh:

Year:  2018        PMID: 29592794     DOI: 10.1080/00324728.2018.1439519

Source DB:  PubMed          Journal:  Popul Stud (Camb)        ISSN: 0032-4728


  2 in total

1.  Stochastic Modeling and Forecasting of Covid-19 Deaths: Analysis for the Fifty States in the United States.

Authors:  Olusegun Michael Otunuga; Oluwaseun Otunuga
Journal:  Acta Biotheor       Date:  2022-09-16       Impact factor: 1.185

2.  Smoothing, Decomposing and Forecasting Mortality Rates.

Authors:  Carlo G Camarda; Ugofilippo Basellini
Journal:  Eur J Popul       Date:  2021-03-25
  2 in total

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