Robust estimate for count time series using GLARMA models: An application to environmental and epidemiological data

The Generalized Linear Autoregressive Moving Average (GLARMA) model has been used in epidemiological studies to evaluate the impact of air pollutants on health. Due to the nature of the data, a robust approach for the GLARMA model is proposed here based on the robustification of the quasi-likelihood function. Outlying observations are bounded separately by weight functions on covariates and the Huber loss function on the response variable. Some technical issues related to the robust approach are discussed and a Monte Carlo study revealed that the robust approach is more reliable than the classic one for contaminated data with additive outliers. The real data analysis investigates the impact of PM10 10 in the number of deaths by respiratory diseases in Vit & oacute;ria, Brazil.