The MortalitySmooth package provides a framework for smoothing count data in both one- and two-dimensional settings. Although general in its purposes, the package is specifically tailored to demographers, actuaries, epidemiologists, and geneticists who may be interested in using a practical tool for smoothing mortality data over ages and/or years. The total number of deaths over a specified age- and year-interval is assumed to be Poisson-distributed, and P-splines and generalized linear array models are employed as a suitable regression methodology. Extra-Poisson variation can also be accommodated
Raw mortality data often exhibit irregular patterns due to randomness. Graduation refers to the act ...
When speaking about data, presuppose its good quality otherwise the accuracy of information would be...
When speaking about data, presuppose its good quality otherwise the accuracy of information would be...
The MortalitySmooth package provides a framework for smoothing count data in both one- and two-dimen...
The MortalitySmooth package provides a framework for smoothing count data in both one- and two-dimen...
We study a smoothing spline Poisson regression model for the analysis of mortality data. Being a non...
The talk gives a gentle, albeit complete introduction to a nonparametric approach for modelling mort...
Description Smoothing one- and two-dimensional Poisson counts with P-splines specifically tailored t...
In this thesis we propose models for estimating and projecting mortality rates using adaptive spline...
Kernel smoothing represents a useful approach in the graduation of mortality rates. Though there exi...
Mortality improvements pose a challenge for the planning of public retirement systems as well as for...
Raw mortality data often exhibit irregular patterns due to randomness. Graduation refers to the act ...
Raw mortality data often exhibit irregular patterns due to randomness. Graduation refers to the act ...
Background: Mortality can be forecast by means of parametric models, principal component methods, an...
Abstract: We suppose that we have mortality data arranged in two tables: one of deaths and the other...
Raw mortality data often exhibit irregular patterns due to randomness. Graduation refers to the act ...
When speaking about data, presuppose its good quality otherwise the accuracy of information would be...
When speaking about data, presuppose its good quality otherwise the accuracy of information would be...
The MortalitySmooth package provides a framework for smoothing count data in both one- and two-dimen...
The MortalitySmooth package provides a framework for smoothing count data in both one- and two-dimen...
We study a smoothing spline Poisson regression model for the analysis of mortality data. Being a non...
The talk gives a gentle, albeit complete introduction to a nonparametric approach for modelling mort...
Description Smoothing one- and two-dimensional Poisson counts with P-splines specifically tailored t...
In this thesis we propose models for estimating and projecting mortality rates using adaptive spline...
Kernel smoothing represents a useful approach in the graduation of mortality rates. Though there exi...
Mortality improvements pose a challenge for the planning of public retirement systems as well as for...
Raw mortality data often exhibit irregular patterns due to randomness. Graduation refers to the act ...
Raw mortality data often exhibit irregular patterns due to randomness. Graduation refers to the act ...
Background: Mortality can be forecast by means of parametric models, principal component methods, an...
Abstract: We suppose that we have mortality data arranged in two tables: one of deaths and the other...
Raw mortality data often exhibit irregular patterns due to randomness. Graduation refers to the act ...
When speaking about data, presuppose its good quality otherwise the accuracy of information would be...
When speaking about data, presuppose its good quality otherwise the accuracy of information would be...