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DBKGrad: An R Package for Mortality Rates Graduation by Discrete Beta Kernel Techniques

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  • Mazza, Angelo
  • Punzo, Antonio

Abstract

We introduce the R package DBKGrad, conceived to facilitate the use of kernel smoothing in graduating mortality rates. The package implements univariate and bivariate adaptive discrete beta kernel estimators. Discrete kernels have been preferred because, in this context, variables such as age, calendar year and duration, are pragmatically considered as discrete and the use of beta kernels is motivated since it reduces boundary bias. Furthermore, when data on exposures to the risk of death are available, the use of adaptive bandwidth, that may be selected by cross-validation, can provide additional benefits. To exemplify the use of the package, an application to Italian mortality rates, for different ages and calendar years, is presented.

Suggested Citation

  • Mazza, Angelo & Punzo, Antonio, 2014. "DBKGrad: An R Package for Mortality Rates Graduation by Discrete Beta Kernel Techniques," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 57(c02).
    • Handle: RePEc:jss:jstsof:v:057:c02
    DOI: http://hdl.handle.net/10.18637/jss.v057.c02
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    References listed on IDEAS

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    1. Luca Bagnato & Antonio Punzo, 2013. "Finite mixtures of unimodal beta and gamma densities and the $$k$$ -bumps algorithm," Computational Statistics, Springer, vol. 28(4), pages 1571-1597, August.
    2. Luca Bagnato & Antonio Punzo & Orietta Nicolis, 2012. "The autodependogram: a graphical device to investigate serial dependences," Journal of Time Series Analysis, Wiley Blackwell, vol. 33(2), pages 233-254, March.
    3. Luca Bagnato & Lucio De Capitani & Antonio Punzo, 2014. "Detecting serial dependencies with the reproducibility probability autodependogram," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 98(1), pages 35-61, January.
    4. Gavin, John & Haberman, Steven & Verrall, Richard, 1993. "Moving weighted average graduation using kernel estimation," Insurance: Mathematics and Economics, Elsevier, vol. 12(2), pages 113-126, April.
    5. Camarda, Carlo G., 2012. "MortalitySmooth: An R Package for Smoothing Poisson Counts with P-Splines," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i01).
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    Cited by:

    1. Juan Manuel Pérez-Salamero González & Marta Regúlez-Castillo & Carlos Vidal-Meliá, 2021. "Differences in Life Expectancy Between Self-Employed Workers and Paid Employees when Retirement Pensioners: Evidence from Spanish Social Security Records," European Journal of Population, Springer;European Association for Population Studies, vol. 37(3), pages 697-725, July.
    2. Shi, Yue & Punzo, Antonio & Otneim, Håkon & Maruotti, Antonello, 2025. "Hidden semi-Markov models for rainfall-related insurance claims," Insurance: Mathematics and Economics, Elsevier, vol. 120(C), pages 91-106.
    3. Punzo, Antonio & Bagnato, Luca & Maruotti, Antonello, 2018. "Compound unimodal distributions for insurance losses," Insurance: Mathematics and Economics, Elsevier, vol. 81(C), pages 95-107.
    4. Shi, Yue & Punzo, Antonio & Otneim, Håkon & Maruotti, Antonello, 2023. "Hidden semi-Markov models for rainfall-related insurance claims," Discussion Papers 2023/17, Norwegian School of Economics, Department of Business and Management Science.
    5. Mazza, Angelo & Punzo, Antonio & McGuire, Brian, 2014. "KernSmoothIRT: An R Package for Kernel Smoothing in Item Response Theory," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i06).
    6. Mahdi Salehi & Andriette Bekker & Mohammad Arashi, 2023. "A Semi-parametric Density Estimation with Application in Clustering," Journal of Classification, Springer;The Classification Society, vol. 40(1), pages 52-78, April.

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