• Communications for Statistical Applications and Methods
• /
• v.24 no.3
• /
• pp.211-226
• /
• 2017

The chain-ladder method, for which run-off data is employed is popularly used in the rate-adjustment and loss-reserving practices of non-life-insurance and health-insurance companies. The method is applicable when the underlying assumption of a consistent development pattern is in regards to a cumulative loss payment after the occurrence of an insurance event. In this study, a modified chain-ladder algorithm is proposed for when the assumption is considered to be only partially appropriate for the given run-off data. The concept of a structural-change point in the run-off data and its reflection in the estimation of unpaid loss amounts are discussed with numerical illustrations. Experience data from private health insurance coverage in Korea were analyzed based on the suggested method. The performance in estimation of loss reserve was also compared with traditional approaches. We present evidence in this paper that shows that a reflection of a structural-change point in the chain-ladder method can improve the risk management of the relevant insurance products. The suggested method is expected to be utilized easily in actuarial practice as the algorithm is straightforward.

• Communications for Statistical Applications and Methods
• /
• v.27 no.6
• /
• pp.675-688
• /
• 2020

In survival analysis of observational data, the inverse probability weighting method and the Cox proportional hazards model are widely used when estimating the causal effects of multiple-valued treatment. In this paper, the two kinds of weights have been examined in the inverse probability weighting method. We explain the reason why the stabilized weight is more appropriate when an inverse probability weighting method using the generalized propensity score is applied. We also emphasize that a marginal hazard ratio and the conditional hazard ratio should be distinguished when defining the hazard ratio as a treatment effect under the Cox proportional hazards model. A simulation study based on real data is conducted to provide concrete numerical evidence.

• The Korean Journal of Applied Statistics
• /
• v.22 no.3
• /
• pp.489-498
• /
• 2009

Underwriting is the first step for the administration of an insurance contract, which may result in stable profitability or unexpected loss for insurance company. Adequacy of underwriting criteria determines underwriting result. Generally, quantitative scoring system is used for underwriting. Method of evaluating risk for the scoring system is summing up scores for risk factors of a potential policyholder in consideration. Scores for each risk factor is predetermined. Current business environment for insurance companies makes underwriting profit more important, which means that insurance companies need more efficient underwriting method. This study suggests a reasonable approach to estimate risk relativities based on generalized linear model. Real data were used to quantify risk levels of groups of insureds for the design of underwriting model. Finally, effects in business volume and profitability of reflecting estimated underwriting scoring system are explained.

• Genomics & Informatics
• /
• v.20 no.1
• /
• pp.8.1-8.14
• /
• 2022

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/ environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.

• Communications for Statistical Applications and Methods
• /
• v.28 no.5
• /
• pp.447-462
• /
• 2021

In insurance, the surrender rate is an important variable that threatens the sustainability of insurers and determines the profitability of the contract. Unlike other actuarial assumptions that determine the cash flow of an insurance contract, however, it is characterized by endogenous variables such as people's economic, social, and subjective decisions. Therefore, a microscopic approach is required to identify and analyze the factors that determine the lapse rate. Specifically, micro-level characteristics including the individual, demographic, microeconomic, and household characteristics of policyholders are necessary for the analysis. In this study, we select panel survey data of Korean Retirement Income Study (KReIS) with many diverse dimensions to determine which variables have a decisive effect on the lapse and apply the lasso regularized regression model to analyze it empirically. As the data contain many missing values, they are imputed using the random forest method. Among the household variables, we find that the non-existence of old dependents, the existence of young dependents, and employed family members increase the surrender rate. Among the individual variables, divorce, non-urban residential areas, apartment type of housing, non-ownership of homes, and bad relationship with siblings increase the lapse rate. Finally, among the financial variables, low income, low expenditure, the existence of children that incur child care expenditure, not expecting to bequest from spouse, not holding public health insurance, and expecting to benefit from a retirement pension increase the lapse rate. Some of these findings are consistent with those in the literature.

• Genomics & Informatics
• /
• v.20 no.2
• /
• pp.16.1-16.12
• /
• 2022

Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.

• Proceedings of the Safety Management and Science Conference
• /
• 2013.04a
• /
• pp.517-531
• /
• 2013

보증 데이터를 통해 제품의 수명 및 형상모수를 추정할 때 최우추정법과 같은 전통적인 통계 분석방법(Classical Statistical Method)을 많이 사용하였다. 그러나 전통적인 통계 분석방법을 통해 수명과 형상모수의 추정 시 표본의 크기가 작거나 불완전한 경우 추정량의 신뢰성이 떨어진다는 단점이 있고 또 누적된 경험과 과거자료를 충분히 이용하지 못하는 단점도 있다. 이러한 문제점을 해결하기 위해 모수의 사전분포를 가정하는 베이지안(Bayesian) 기법의 적용이 필요하다. 하지만 보증 데이터분석에 있어서 베이지안 기법을 이용한 연구는 아직 미흡한 실정이다. 본 연구에서는 수명분포가 와이블 분포를 갖는 보증데이터를 활용하여 모수 추정의 효율성을 비교 분석하고자 한다. 이를 위해 와이블 분포의 모수가 대수정규분포를 따르는 사전분포를 갖는 베이지안 기법과 전통적 통계기법인 생명표법(Actuarial method)을 활용하여 추정량을 도출하고 비교 분석하였다. 이를 통해 충분한 관측 데이터를 확보할 수 없는 경우에 베이지안 기법을 이용한 보증 데이터 분석방법의 성능을 확인하고자 한다.

• Journal of the Korean Society for Industrial and Applied Mathematics
• /
• v.28 no.3
• /
• pp.88-95
• /
• 2024

In this article, we propose an efficient and accurate adaptive time-stepping numerical method for the Black-Scholes (BS) equations. The numerical scheme used is the finite difference method (FDM). The proposed adaptive time-stepping computational scheme is based on the maximum norm of the discrete Laplacian values of option values on a discrete domain. Most numerical solvers for the BS equations require a small time step when there are large variations in the solutions. To resolve this problem, we propose an adaptive time-stepping algorithm that uses a small time step size when the maximum norm of the discrete Laplacian values on a discrete domain is large; otherwise, a larger time step size is used to speed up the computation. To demonstrate the high performance of the proposed adaptive time-stepping methodology, we conduct several computational experiments. The numerical tests confirm that the proposed adaptive time-stepping method improves both the efficiency and accuracy of computations for the BS equations.

• Journal of Korean Public Health Nursing
• /
• v.31 no.1
• /
• pp.5-18
• /
• 2017

Purpose: This study was conducted to suggest a method for financial projection of health insurance expenditures that reflects future changes in demographic structure. Methods: Using data associated with the number of patients and health insurance cost per patient, generalized linear models (GLM) were fitted with demographic explanatory variables. Models were constructed separately for individual medical departments, types of medical service, and types of public health insurance. Goodness-of-fit of most of the applied GLM models was quite satisfactory. By combining estimates of frequency and severity from the constructed models and results of the population projection, total annual health insurance expenditures were projected through year 2060. Results: Expenditures for medical departments associated with diseases that are more frequent in elderly peoples are expected to increase steeply, leading to considerable increases in overall health insurance expenditures. The suggested method can contribute to improvement of the accuracy of financial projection. Conclusion: The overall demands for medical service, medical personnel, and relevant facilities in the future are expected to increase as the proportion of elderly people increases. Application of a more reasonable estimation method reflecting changes in demographic structure will help develop health policies relevant to above mentioned resources.

• The Korean Journal of Applied Statistics
• /
• v.31 no.6
• /
• pp.761-769
• /
• 2018

Simulations are important for survival analyses that deal with censored data. Cox models are widely used in survival analyses, therefore, we investigate how to generate censored data that can simulate the Cox model. Bender et al. (Statistics in Medicine, 24, 1713-1723, 2005) provided a parametric method for generating survival times, but we need to generate censoring times as well as survival times to simulate the censored data. In addition to the parametric method for generating censored data, a nonparametric method is also proposed and applied to a real data set.