On fitting generalized linear and non-linear models of mortality

Currie, Iain D.

doi:10.1080/03461238.2014.928230

Cited by 108 publications

(95 citation statements)

References 19 publications

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“…In this paper, we build upon the works of Hunt and Blake (2015) and Currie (2014) to define the family of Generalised Age-Period-Cohort stochastic mortality models by mirroring the terminology of generalised linear models. We also introduce the R package StMoMo 1 which exploits the unifying framework of the Generalised Age-Period-Cohort family to provide computational tools for implementing many of the stochastic mortality models proposed to date.…”

Section: Introductionmentioning

confidence: 99%

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StMoMo: An R Package for Stochastic Mortality Modelling

2015

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In this paper we mirror the framework of generalised (non-)linear models to define the family of generalised Age-Period-Cohort stochastic mortality models which encompasses the vast majority of stochastic mortality projection models proposed to date, including the well-known Lee-Carter and Cairns-Blake-Dowd models. We also introduce the R package StMoMo which exploits the unifying framework of the generalised Age-Period-Cohort family to provide tools for fitting stochastic mortality models, assessing their goodness of fit and performing mortality projections. We illustrate some of the capabilities of the package by performing a comparison of several stochastic mortality models applied to the England and Wales population.

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Section: Introductionmentioning

confidence: 99%

“…Hunt and Blake (2015) review the structure of mortality models and describe an Age-Period-Cohort model structure which encompasses the vast majority of stochastic mortality models. Currie (2014) shows that many mortality models can be expressed in terms of generalised linear models or generalised non-linear models.…”

Section: Introductionmentioning

confidence: 99%

StMoMo: An R Package for Stochastic Mortality Modelling

2015

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“…In such a case, the model does not achieve convergence at all. These results have been confirmed by Lovász (2011) andCurrie (2016). It is thought that the convergence problem is due to some sort of identifiability issue in the likelihood function.…”

Section: Problems In Cohort Extended Modelsmentioning

confidence: 58%

“…• Data for the USA (Cairns et al (2009) andCurrie (2016)) • Data for Netherlands (van Berkum et al, forthcoming) • Data for Spain (Debón et al (2010)) The results with inclusion of the additional constraint for these datasets, as well as for the England and Wales Males and Females are summarized in the following table 4. 691 29,530 In Hunt and Villegas (2015) the authors note the other data-sets show many of the same issues with robustness and stability as those in the data for England and Wales Males in models M and H1.…”

Section: Hunt and Villegas (2015): Tests On Other Datasetsmentioning

confidence: 99%

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The Convergence and Robustness of Cohort Extensions of Mortality Models

Kennes¹

2017

MARBLE

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The prediction of future of future lifespans of society is known to be very difficult to predict. The most efficient and effective method at the moment of writing is known as the Lee Carter model. However, it has been debated that the intrinsic simplicity of the Lee Carter model and its non-usage of societal trends is not too simple for a complex process such as the average lifespan of currently living people. A strong argument and example can be found in (un)healthy habits of society like smoking and exercise. The simplicity of the Lee Carter model extended with a more abstract form of this structural information gives rise to the cohort extended mortality models. And, although the explanatory power and the underlying rationality might theoretically be valid, in practice it appears that this extension of the model makes the fitting procedure more complex and thus affects the robustness of the model negatively. A possible solution was proposed by Hunt and Villegas (2016) and applied in this paper. Although the theoretical aspect of their solution seems valid, the empirical results show that it is a step in the right direction, but not yet a perfect solution.

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Identifying subgroups of age and cohort effects in obesity prevalence

Miljkovic

Wang

2020

Biometrical J

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The obesity epidemic represents an important public health issue in the United States. Studying obesity trends across age groups over time helps to identify crucial relationships between the disease and medical treatment allowing for the development of effective prevention policies. We aim to define subgroups of age and cohort effects in obesity prevalence over time by considering an optimization approach applied to the age‐period‐cohort (APC) model. We consider a heterogeneous regression problem where the regression coefficients are age dependent and belong to subgroups with unknown grouping information. Using the APC model, we apply the alternating direction method of multipliers (ADMM) algorithm to develop a two‐step algorithm for (1) subgrouping of cohort effects based on similar characteristics and (2) subgrouping age effects over time. The proposed clustering approach is illustrated for the United States population, aged 18–79, during the period 1990–2017.

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On fitting generalized linear and non-linear models of mortality

Cited by 108 publications

References 19 publications

StMoMo: An R Package for Stochastic Mortality Modelling

StMoMo: An R Package for Stochastic Mortality Modelling

The Convergence and Robustness of Cohort Extensions of Mortality Models

Identifying subgroups of age and cohort effects in obesity prevalence

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