Nadarajah, Saralees; Kwofie, Charles Heavy tailed modeling of automobile claim data from Ghana. (English) Zbl 1478.62313 J. Comput. Appl. Math. 405, Article ID 113947, 13 p. (2022). Summary: Africa has long been ignored with respect to among others insurance modeling. In this paper, we propose a model for automobile claim data from Ghana. The body of the data are modeled by a lognormal distribution. However, the tail is noted be too heavy to be modeled by a single heavy tailed distribution. A mixture of distributions is used to model the tail. Estimates of risk are given. MSC: 62P05 Applications of statistics to actuarial sciences and financial mathematics Keywords:inverse Burr distribution; lognormal distribution; mixture Software:R; CompLognormal PDFBibTeX XMLCite \textit{S. Nadarajah} and \textit{C. Kwofie}, J. Comput. Appl. Math. 405, Article ID 113947, 13 p. (2022; Zbl 1478.62313) Full Text: DOI References: [1] Cooray, K.; Ananda, M. M.A., Modeling actuarial data with a composite lognormal-Pareto model, Scand. Actuar. J., 321-334 (2005) · Zbl 1143.91027 [2] Scollnik, D. P.M., On composite lognormal-Pareto models, Scand. Actuar. J., 20-33 (2007) · Zbl 1146.91028 [3] Teodorescu, S., On the truncated composite lognormal-Pareto model, Math. Rep., 12, 71-84 (2010) · Zbl 1224.60022 [4] Lee, K.-H., Estimation on composite lognormal-Pareto distribution based on doubly censored samples, J. Korean Data Inf. Sci. Soc., 22, 171-177 (2011) [5] Pigeon, M.; Denuit, M., Composite lognormal-Pareto model with random threshold, Scand. Actuar. J., 177-192 (2011) · Zbl 1277.62258 [6] Eliazar, I. I.; Cohen, M. H., A langevin approach to the log-Gauss-Pareto composite statistical structure, Physica A, 391, 5598-5610 (2012) [7] Nadarajah, S.; Bakar, S. A.A., CompLognormal: AN R package for composite lognormal distributions, R J., 5, 97-103 (2013) [8] Nadarajah, S.; Bakar, S. A.A., New composite models for the Danish fire insurance data, Scand. Actuar. J., 180-187 (2014) · Zbl 1401.91177 [9] Pak, R. J., A robust estimation for the composite lognormal-Pareto model, Commun. Stat. Appl. Methods, 20, 311-319 (2013) [10] Bee, M., Estimation of the lognormal-Pareto distribution using probability weighted moments and maximum likelihood, Comm. Statist. Simulation Comput., 44, 2040-2060 (2015) · Zbl 1328.62114 [11] Cooray, K.; Cheng, C.-I., Bayesian estimators of the lognormal-Pareto composite distribution, Scand. Actuar. J., 500-515 (2015) · Zbl 1401.91120 [12] Kim, B.; Noh, J.; Baek, C., Threshold estimation for the composite lognormal-GPD models, Korean J. Appl. Stat., 29, 807-822 (2016) [13] Luckstead, J.; Devadoss, S., Pareto tails and lognormal body of US cities size distribution, Physica A, 465, 573-578 (2017) [14] Park, S.; Baek, C., Time-varying modeling of the composite LN-GPD, Korean J. Appl. Stat., 31, 109-122 (2018) [15] Mutali, S.; Vernic, R., On the composite lognormal-Pareto distribution with uncertain threshold, Comm. Statist. Simulation Comput. (2020) [16] R Development Core Team, A Language and Environment for Statistical Computing (2021), R Foundation for Statistical Computing: R Foundation for Statistical Computing Vienna, Austria [17] Akaike, H., A new look at the statistical model identification, IEEE Trans. Automat. Control, 19, 716-723 (1974) · Zbl 0314.62039 [18] Schwarz, G. E., Estimating the dimension of a model, Ann. Statist., 6, 461-464 (1978) · Zbl 0379.62005 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.