Predicting the Frequency and Amount of Health Care Expenditures

Frees, Fsa Edward W.; Gao, Jie; Rosenberg, Fsa Marjorie A.

doi:10.1080/10920277.2011.10597626

Cited by 64 publications

(38 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An advantage of this approach is that the frequency can be used as a covariate to model the average severity. See [51] for a healthcare application of this approach. For another application, a Bayesian approach for modeling claim frequency and size was proposed in [52], with both covariates as well as spatial random effects taken into account.…”

Section: Frequency Severity Dependency Modelsmentioning

confidence: 99%

Multivariate Frequency-Severity Regression Models in Insurance

Frees

Lee

Yang

2016

Risks

110

View full text Add to dashboard Cite

Abstract:In insurance and related industries including healthcare, it is common to have several outcome measures that the analyst wishes to understand using explanatory variables. For example, in automobile insurance, an accident may result in payments for damage to one's own vehicle, damage to another party's vehicle, or personal injury. It is also common to be interested in the frequency of accidents in addition to the severity of the claim amounts. This paper synthesizes and extends the literature on multivariate frequency-severity regression modeling with a focus on insurance industry applications. Regression models for understanding the distribution of each outcome continue to be developed yet there now exists a solid body of literature for the marginal outcomes. This paper contributes to this body of literature by focusing on the use of a copula for modeling the dependence among these outcomes; a major advantage of this tool is that it preserves the body of work established for marginal models. We illustrate this approach using data from the Wisconsin Local Government Property Insurance Fund. This fund offers insurance protection for (i) property; (ii) motor vehicle; and (iii) contractors' equipment claims. In addition to several claim types and frequency-severity components, outcomes can be further categorized by time and space, requiring complex dependency modeling. We find significant dependencies for these data; specifically, we find that dependencies among lines are stronger than the dependencies between the frequency and average severity within each line.

show abstract

Section: Frequency Severity Dependency Modelsmentioning

confidence: 99%

Multivariate Frequency-Severity Regression Models in Insurance

Frees

Lee

Yang

2016

Risks

110

View full text Add to dashboard Cite

show abstract

“…In this study, an alternative predictor for the total claim amount of individuals is suggested for non-life insurance pricing. Differently from Frees et al [13], this study focuses on marginal predictions considering the difficulty of following the same policyholders over the years for yearly renewable insurance contracts. The objective is to obtain marginal predictions of total claim amounts by taking account of heterogeneity and to model claim amounts in original scale instead of using log transformation.…”

Section: Introductionmentioning

confidence: 99%

“…Frees et al [13] used aggregate loss approach to predict annual inpatient and outpatient expenditures of individuals. They used LMM for the log-transformed expenditures per event and NB model for the number of events.…”

Section: Introductionmentioning

confidence: 99%

“…However in practice this assumption is not realistic since claim severity is expected to be affected by claim frequency. One way of modeling the dependence between these two components is to model claim severity conditional on the number of claims and to include the number of claims as an explanatory variable in severity model ( [13], [16], [28]). …”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Incorporating heterogeneity into the prediction of total claim amount

Acar¹,

Karabey²,

Gregori³

2017

HJMS

View full text Add to dashboard Cite

This paper proposes an alternative predictor for the total claim amount of individuals that can be used for any type of non-life insurance products in which individuals may have multiple claims within one policy period. The impact of heterogeneity on expected total claim amount is investigated focusing on marginal predictions. Generalized linear mixed model (GLMM) is used for the amounts of loss per claim. Closedform expression of the predictor is derived using marginal mean under GLMM and claim count distribution. Empirical studies are performed using a private health insurance data set of a Turkish insurance company. Proposed predictive model provides the lowest prediction errors among competing models according to the mean absolute error criterion.

show abstract

“…Medical expenditures for 39 Following the literature on health cost (e.g., Pohlmeier and Ulrich (1995), Diehr et al (1999), Buntin and Zaslavsky (2004), and Frees et al (2011)), I consider the process that generates zero medical expenditures different from the one that generates positive medical expenditures. Casanova (2010) also considers two separate processes that generate medical expenditures.…”

Section: Total Medical Expenditurementioning

confidence: 99%

Happy Together or Home Alone: A Structural Model of the Role of Health Insurance in Household Joint Retirement

Guo

View full text Add to dashboard Cite

Predicting the Frequency and Amount of Health Care Expenditures

Cited by 64 publications

References 16 publications

Multivariate Frequency-Severity Regression Models in Insurance

Multivariate Frequency-Severity Regression Models in Insurance

Incorporating heterogeneity into the prediction of total claim amount

Happy Together or Home Alone: A Structural Model of the Role of Health Insurance in Household Joint Retirement

Contact Info

Product

Resources

About