A copula model for marked point processes

Diao, Liqun; Cook, Richard J.; Lee, Ker‐Ai

doi:10.1007/s10985-013-9259-3

Cited by 6 publications

(6 citation statements)

References 44 publications

(19 reference statements)

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“…For instance, Ver Hoef and Jansen (2007) use both mixture and hurdle models for zero-inflated seal haul out analyses. More recent zero-inflation research focuses on the joint modelling of multiple outcomes (Diao et al 2013;Feng and Dean 2012;Hatfield et al 2012;Rodrigues-Motta et al 2013). For instance Feng and Dean (2012) use a latent random risk term to link the spatial component across outcomes and Dean and Lundy (pers.comm., Lundy's thesis dissertation, in preparation, 2015) model juvenile delinquent behaviour using a longitudinal joint model.…”

Section: Discussionmentioning

confidence: 99%

Advances and Challenges in Parametric and Semi-parametric Analysis for Correlated Data

Sutradhar¹

2016

Lecture Notes in Statistics

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Section: Discussionmentioning

confidence: 99%

Advances and Challenges in Parametric and Semi-parametric Analysis for Correlated Data

Sutradhar¹

2016

Lecture Notes in Statistics

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“…Diao et al. 11 proposed a copula model to link the longitudinal data with a recurrent event process. Recently, literary works 12,13 proposed to use a continuous-time multistate model to deal with informative observation process.…”

Section: Introductionmentioning

confidence: 99%

Accounting for informative observation process in transition models of binary longitudinal outcome: Application to medical record data

Bible,

St. Ville,

Albert

et al. 2024

Stat Methods Med Res

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When extracting medical record data to form a retrospective cohort, investigators typically focus on a pre-specified study window, and select subjects who had hospital visits during that study window. However, such data extraction may suffer from an informative observation process, since sicker patients may have hospital visits more frequently. For example, Consecutive Pregnancy Study is a retrospective cohort study of women with multiple pregnancies in 23 Utah hospitals from 2003 to 2010, where the interest is to understand the risk factors of recurrent pregnancy outcomes, such as preterm birth. The observation process is informative in the sense that, women with adverse pregnancy outcomes may be less likely/willing/able to endure subsequent pregnancies. We proposed a three-part joint model with shared random effects structure to address this analytic complication. Particularly, a first-order transition model is used to model the longitudinal binary outcome; a gamma regression model is assumed for the inter-pregnancy intervals; a continuation ratio model specifies the probability of continuing with more births in the future. We note that the latter two parts give rise to a parametric cure-rate survival model. The performance of the proposed method was examined in extensive simulation studies, with both correctly and mis-specified models. The analyses of Consecutive Pregnancy Study data further demonstrate the inadequacies of fitting the transition model alone ignoring the informative observation process.

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“…However, this simplifying assumption is often questionable in applications (see, e.g. Diao et al., 2013; Schlather et al., 2004; Schoenberg, 2004). The proposed copula approach allows for flexible marginal models for the event time and mark, and is straightforward to implement in practice.…”

Section: Introductionmentioning

confidence: 99%

Assessing Hail Risk for Property Insurers with a Dependent Marked Point Process

Shi

Fung

Dickinson

2021

Journal of the Royal Statistical Society Series A: Statistics in Society

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Hail risk is among the most challenging perils to insure and property damage due to hailstones has been on the top of the list of annual claims for most non‐life insurers. In this article, we present a simple yet flexible statistical model for insurers to assess and manage hail risks from two aspects: analysing the insurance claims arrival pattern upon occurrence of a hailstorm and quantifying the subsequent financial impact of the hailstorm. We formulate the problem using a marked point process where the reporting of insurance claims due to a hailstorm is treated as recurrent events and the claim amounts are viewed as associated marks. Three complications are addressed in model building: the unobserved heterogeneity in claim arrival, the dependence between the event time and the mark and the complex distribution in claim amount. Using a unique data that combine the exposure data from a major US insurer and the radar data from a third‐party vendor, we show the proposed method help improve predictive analytics for post‐hailstorm claims volume, arrival rate and severity, and thus claim management decisions for the insurer.

show abstract

A copula model for marked point processes

Cited by 6 publications

References 44 publications

Advances and Challenges in Parametric and Semi-parametric Analysis for Correlated Data

Advances and Challenges in Parametric and Semi-parametric Analysis for Correlated Data

Accounting for informative observation process in transition models of binary longitudinal outcome: Application to medical record data

Assessing Hail Risk for Property Insurers with a Dependent Marked Point Process

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