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Mortality Estimation and Forecasting With Smoothing and Overdispersion
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The ESRC Business and Local Government Data Research Centre in conjunction with SASNet invites you to participate in a free training seminar on Mortality Estimation and Forecasting With Smoothing and Overdispersion, featuring Peter W. F. Smith, Professor of Social Statistics at the University of Southampton.
In this training course, Peter W. F. Smith will discuss the current issues with existing models as outlined below, and explain in greater detail how the proposed modelling framework accounts for these. This seminar is aimed at attendees who are interested in estimating and forecasting mortality rates as well as statistical modelling or population processes more generally. Some prior knowledge of statistical modelling would be useful, as Peter will present some model equations, although he will explain the ideas in graphs and words as well.
Another feature, lacking in many existing approaches, is the facility to impose smoothness in parameter series which vary over age, cohort and time. Such constraints are integrated into the modelling process, so that there is a natural feedback, whereby the smoothing of parameter series can appropriately impact other estimates, rather than being performed in a post hoc fashion. Peter will discuss how the proposed modelling framework demonstrates that generalised additive models (GAMs) can be used for mortality modelling and forecasting. GAMs are a flexible class of semiparametric statistical models which allow parametric functions and unstructured (but smooth) functions of explanatory variables to appear in the model simultaneously. In particular, GAMs allow us to differentially smooth components, such as cohorts, more aggressively in areas of sparse data for the component concerned. While GAMs can provide a reasonable fit for the ages where there is adequate data, estimation and extrapolation of mortality rates using a GAM at higher ages is problematic due to high variation in crude rates. At these ages, parametric models can give a more robust fit, enabling a borrowing of strength across age groups. Peter and colleague’s modelling methodology is based on a smooth transition between a GAM at lower ages and a fully parametric model at higher ages. Finally, their framework is fully probabilistic, and provides a coherent description of forecast uncertainty.
Prof. Peter W. F. Smith
University of Essex
Intermediate (some prior knowledge)
Website and registration
Prof. Peter W. F. Smith
Mortality Estimation , Forecasting , Overdispersion
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