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Giancarlo Camarda, Modelling Mortality over Age and Time, and Age and Cohort: A Nonparametric Approach
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por : Demography Today on Jun 25, 2018
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Duración: 00:48:52

Giancarlo Camarda, Modelling Mortality over Age and Time, and Age and Cohort: A Nonparametric Approach - Descripción

The talk gives a gentle, albeit complete introduction to a nonparametric approach for modelling mortality over age and time, as well as over age and cohort. Specifically, mortality developments normally show regular patterns, so smoothing approaches are a natural choice to analyze mortality changes. Moreover smoothed one- and two-dimensional trends can then be used for further analysis. Though the talk places specific emphasis on P-spline models, a gradual journey toward such methods is offered. A first part is devoted to the intuitive and plain direct smoothing, using simple, self-explanatory examples. The talk then moves on to the idea of penalty, which will be crucial in setting up more sophisticated methods. rnStarting from simple regression problems, P-splines are presented as a flexible and versatile methodology to smooth aggregate mortality data. Specifically P-spline models combine (fixed-knot) B-splines with a roughness penalty. A relatively large number of B-splines provide enough flexibility to capture trends. The additional penalty reduces the number of parameters leading to a rather parsimonious model with smoothed trends. An important advantage of the P-spline lies in its straightforward generalization when data are presented in a rectangular array. This is the case of mortality over age and either years or cohorts. Moving between these two data structures (age vs. years and age vs. cohort) can be achieved by simple manipulation of the data without changing the general architecture of the methodology. Finally, examples of these settings will be presented.

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Giancarlo Camarda, Modelling Mortality over Age and Time, and Age and Cohort: A Nonparametric Approach - Biografía

Carlo Giovanni Camarda is a research scientist at the Institut National d’Etudes Démographiques (INED) in Paris (France). From 2007 to 2012, he was a research scientist at the Max Planck Institute for Demographic Research in Rostock (Germany). He completed his first degree at the Sapienza University of Rome (Italy) in Social and Demographic Statistical Sciences, then went on to study at the Max Planck Institute for Demographic Research in the Laboratory of Statistical Demography. He earned a PhD in Mathematical Engineering: Statistical Sciences and Techniques Area from the Universidad Carlos III dernMadrid (UC3M) with a dissertation on “Smoothing Methods for the Analysis of Mortality Development.” His interests range from the general theory of the biodemography of human ageing to modeling patterns of digit preference, warping models for lifetime distributions, modelling and smoothing mortality surfaces, reconstruction of mortality series by causes of death, and analysis of social contact data. He has also collaborated with a number of European research institutions and engaged in various teaching activities. In recent years, he has written research papers in the demography and statistics field and has devised an R package for smoothing and forecasting mortality.

 

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