Support Vector Machines as tools for mortality graduation

Kostaki, Anastasia; Moguerza, Javier M.; Olivares, Alberto; Psarakis, Stelios

doi:10.25336/p6vs46

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…In the insurance literature, SVMs have been used as a mortality graduation technique (Kostaki et al 2011). They could equally well be used to model other transition probabilities, such as disability inception or termination rates.…”

Section: Introductionmentioning

confidence: 99%

Quantum Support Vector Regression for Disability Insurance

Djehiche¹,

Löfdahl²

2021

Risks

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We propose a hybrid classical-quantum approach for modeling transition probabilities in health and disability insurance. The modeling of logistic disability inception probabilities is formulated as a support vector regression problem. Using a quantum feature map, the data are mapped to quantum states belonging to a quantum feature space, where the associated kernel is determined by the inner product between the quantum states. This quantum kernel can be efficiently estimated on a quantum computer. We conduct experiments on the IBM Yorktown quantum computer, fitting the model to disability inception data from a Swedish insurance company.

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Section: Introductionmentioning

confidence: 99%

Quantum Support Vector Regression for Disability Insurance

Djehiche¹,

Löfdahl²

2021

Risks

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“…In the insurance literature, SVMs have been used as a mortality graduation technique [5]. They could equally well be used to model other transition probabilities, such as disability inception or termination rates.…”

Section: Introductionmentioning

confidence: 99%

Quantum support vector regression for disability insurance

Djehiche¹,

Löfdahl²

2021

Preprint

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We propose a hybrid classical-quantum approach for modeling transition probabilities in health and disability insurance. The modeling of logistic disability inception probabilities is formulated as a support vector regression problem. Using a quantum feature map, the data is mapped to quantum states belonging to a quantum feature space, where the associated kernel is determined by the inner product between the quantum states. This quantum kernel can be efficiently estimated on a quantum computer. We conduct experiments on the IBM Yorktown quantum computer, fitting the model to disability inception data from a Swedish insurance company.

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Application of P-TOPALS for Smoothing Input Data for Population Projections ‘At the Edge’

Dyrting,

Taylor,

Wilson

2024

Popul Res Policy Rev

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Sparsely populated areas of developed countries are regions of great demographic diversity and dynamism. While they remain strategically and economically important, trends in urbanization and technology have increased their relative sparsity and isolation making centralized government, service delivery and planning a challenge. Populations of their sub-jurisdictions are small and often exhibit significant heterogeneity in key demographic characteristics, not least between their Indigenous first residents and non-Indigenous citizens. Development of projection models for these areas is challenged by significant input data paucity, biases and structural issues related to the data collection and estimation architectures in place to gather input data across diverse and small populations. While this is the case, the demand for and importance of projections is no less for sparsely populated areas than elsewhere. Variants of the cohort component model are important tools for population projections for SPAs, with their grounding in the demographic accounting equation and modest input requirements. Nevertheless, to attain fit-for-purpose input data requires demographers to consider and select from a growing number of methods for smoothing issues with input data for projections for these regions. In this article we analyze the contributions of recent advances in methods for estimating fertility, mortality, and migration rates for small and diverse populations such as those in SPAs, focusing on the very sparsely populated jurisdiction of the Northern Territory of Australia. In addition to the contributions of our method itself, results at the detailed level demonstrate how abnormal and challenging ‘doing’ projections for sparsely populated areas can be.

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Support Vector Machines as tools for mortality graduation

Cited by 5 publications

References 24 publications

Quantum Support Vector Regression for Disability Insurance

Quantum Support Vector Regression for Disability Insurance

Quantum support vector regression for disability insurance

Application of P-TOPALS for Smoothing Input Data for Population Projections ‘At the Edge’

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