Semiparametric generalized linear models for time-series data

TL;DR

This paper introduces a semiparametric generalized linear model framework for time-series data that avoids specifying a response distribution, offering robustness and comparable performance to parametric models, especially under misspecification.

Contribution

It develops a novel semiparametric approach for time-series analysis that estimates the response distribution nonparametrically alongside other parameters.

Findings

Method performs well in simulations, comparable to parametric models.

Robustness under model misspecification is demonstrated.

Applied to real datasets, showing practical utility.

Abstract

Time-series data in population health and epidemiology often involve non-Gaussian responses. In this note, we propose a semiparametric generalized linear models framework for time-series data that does not require specification of a working conditional response distribution for the data. Instead, the underlying response distribution is treated as an infinite-dimensional parameter which is estimated simultaneously with the usual finite-dimensional parameters via a maximum empirical likelihood approach. A general consistency result for the resulting estimators is given. Simulations suggest that both estimation and inferences using the proposed method can perform as well as correctly-specified parametric models even for moderate sample sizes, but can be more robust than parametric methods under model misspecification. The method is used to analyse the Polio dataset from Zeger (1988) and a recent Kings Cross assault dataset from Menendez et al. (2015).