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

TL;DR

The paper introduces the R package DBKGrad, which applies fixed and adaptive discrete beta kernel techniques for mortality rate graduation, addressing computational gaps and enabling subsequent analysis.

Contribution

It provides a novel R package implementing discrete beta kernel methods with adaptive smoothing and confidence intervals for mortality rate graduation.

Findings

Automatic boundary bias reduction

Flexible bandwidth selection via cross-validation

Application to Sicily mortality data

Abstract

Kernel smoothing represents a useful approach in the graduation of mortality rates. Though there exist several options for performing kernel smoothing in statistical software packages, there have been very few contributions to date that have focused on applications of these techniques in the graduation context. Also, although it has been shown that the use of a variable or adaptive smoothing parameter, based on the further information provided by the exposed to the risk of death, provides additional benefits, specific computational tools for this approach are essentially absent. Furthermore, little attention has been given to providing methods in available software for any kind of subsequent analysis with respect to the graduated mortality rates. To facilitate analyses in the field, the R package DBKGrad is introduced. Among the available kernel approaches, it considers a recent discrete beta kernel estimator, in both its fixed and adaptive variants. In this approach, boundary bias is automatically reduced and age is pragmatically considered as a discrete variable. The bandwidth, fixed or adaptive, is allowed to be manually given by the user or selected by cross-validation. Pointwise confidence intervals, for each considered age, are also provided. An application to mortality rates from the Sicily Region (Italy) for the year 2008 is also presented to exemplify the use of the package.