Discrete Weibull generalised additive model: an application to count fertility data

TL;DR

This paper introduces a discrete Weibull-based generalized additive model for count data, effectively capturing complex dependencies in fertility plans with advantages over existing non-parametric methods.

Contribution

It proposes a novel discrete Weibull generalized additive model that models over- and under-dispersed count data efficiently, avoiding crossing quantiles and leveraging existing software.

Findings

Model captures complex tail dependencies in fertility data.

Provides comparable effects to jittering without crossing quantiles.

Offers computational efficiency with existing R packages.

Abstract

Fertility plans, measured by the number of planned children, have been found to be affected by education and family background via complex tail dependencies. This challenge was previously met with the use of non-parametric jittering approaches. This paper shows how a novel generalized additive model based on a discrete Weibull distribution provides partial effects of the covariates on fertility plans which are comparable to jittering, without the inherent drawback of crossing conditional quantiles. The model has some additional desirable features: both over- and under-dispersed data can be modelled by this distribution, the conditional quantiles have a simple analytic form and the likelihood is the same of that of a continuous Weibull distribution with interval-censored data. The latter means that efficient implementations are already available, in the R package gamlss, for a range of models and inferential procedures, and at a fraction of the time compared to the jittering and COM-Poisson approaches, showing potential for the wide applicability of this approach to the modelling of count data.