Ozaki, Vitor A.; Silva, Ralph S. Bayesian ratemaking procedure of crop insurance contracts with skewed distribution. (English) Zbl 1473.62352 J. Appl. Stat. 36, No. 4, 443-452 (2009). Summary: Over the years, crop insurance programs became the focus of agricultural policy in the USA, Spain, Mexico, and more recently in Brazil. Given the increasing interest in insurance, accurate calculation of the premium rate is of great importance. We address the crop-yield distribution issue and its implications in pricing an insurance contract considering the dynamic structure of the data and incorporating the spatial correlation in the Hierarchical Bayesian framework. Results show that empirical (insurers) rates are higher in low risk areas and lower in high risk areas. Such methodological improvement is primarily important in situations of limited data. Cited in 3 Documents MSC: 62P05 Applications of statistics to actuarial sciences and financial mathematics 62F15 Bayesian inference Keywords:crop insurance; Bayesian hierarchical model; premium rate; skew-normal distribution; spatial correlation PDFBibTeX XMLCite \textit{V. A. Ozaki} and \textit{R. S. Silva}, J. Appl. Stat. 36, No. 4, 443--452 (2009; Zbl 1473.62352) Full Text: DOI References: [1] DOI: 10.1111/1467-8276.00495 · doi:10.1111/1467-8276.00495 [2] Azzalini A., Scand. J. Statist. 12 pp 171– (1985) [3] DOI: 10.2307/1243713 · doi:10.2307/1243713 [4] Besag J., J. Royal Statist. Soc. Ser. B 36 pp 192– (1974) [5] DOI: 10.2307/1243742 · doi:10.2307/1243742 [6] DOI: 10.2307/1243715 · doi:10.2307/1243715 [7] DOI: 10.1080/01621459.1989.10478783 · Zbl 1248.62211 · doi:10.1080/01621459.1989.10478783 [8] DOI: 10.2307/1236284 · doi:10.2307/1236284 [9] DOI: 10.2307/1242190 · doi:10.2307/1242190 [10] DOI: 10.1093/biomet/85.1.1 · Zbl 0904.62036 · doi:10.1093/biomet/85.1.1 [11] DOI: 10.1201/9780203492000 · doi:10.1201/9780203492000 [12] DOI: 10.2307/3180276 · doi:10.2307/3180276 [13] DOI: 10.2307/1244441 · doi:10.2307/1244441 [14] Henze H., Scand. J. Statist. 13 pp 271– (1986) [15] IBGE – Statistical and Geography Brazilian Institute http://www.ibge.gov.br/english [16] DOI: 10.2307/1244582 · doi:10.2307/1244582 [17] DOI: 10.1111/1467-8276.00120 · doi:10.1111/1467-8276.00120 [18] DOI: 10.1111/0002-9092.00039 · doi:10.1111/0002-9092.00039 [19] DOI: 10.2307/1243993 · doi:10.2307/1243993 [20] DOI: 10.2307/1241595 · doi:10.2307/1241595 [21] DOI: 10.1080/00036840600749680 · doi:10.1080/00036840600749680 [22] DOI: 10.1111/j.1467-8276.2008.01153.x · doi:10.1111/j.1467-8276.2008.01153.x [23] DOI: 10.2307/1243953 · doi:10.2307/1243953 [24] DOI: 10.1111/1467-8276.00106 · doi:10.1111/1467-8276.00106 [25] DOI: 10.1111/j.0092-5853.2004.00587.x · doi:10.1111/j.0092-5853.2004.00587.x [26] DOI: 10.1016/S0731-9053(04)18010-9 · doi:10.1016/S0731-9053(04)18010-9 [27] DOI: 10.2307/1243160 · doi:10.2307/1243160 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.