2021
DOI: 10.1590/0101-7438.2021.041.00241120
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Generalized Additive Model for Count Time Series: An Application to Quantify the Impact of Air Pollutants on Human Health

Abstract: The generalized additive model (GAM) has been used in many epidemiological studies where frequently the response variable is a nonnegative integer-valued time series. However, GAM assume that the observations are independent, which is generally not the case in time series. In this paper, an autoregressive moving average (ARMA) component is incorporated to the GAM. The resulting GAM-ARMA model is based on the generalized linear autoregressive moving average (GLARMA) model where some linear components are replac… Show more

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Cited by 2 publications
(2 citation statements)
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“…Larger penalties shrink the coefficient covariances, effectively forcing the function towards a straight line when the data do not justify a nonlinear relationship (Marra & Wood, 2011; Wood, 2016). GAMs are particularly sought after for modelling time series to identify nonlinear or time‐varying covariate effects, perform smoothing of historical time series and uncover periods of rapid change, though strong temporal autocorrelation can make it challenging to estimate key parameters (Camara et al, 2021; Knape, 2016; Simpson, 2018; Spooner et al, 2018; Yang et al, 2012).…”
Section: Introductionmentioning
confidence: 99%
“…Larger penalties shrink the coefficient covariances, effectively forcing the function towards a straight line when the data do not justify a nonlinear relationship (Marra & Wood, 2011; Wood, 2016). GAMs are particularly sought after for modelling time series to identify nonlinear or time‐varying covariate effects, perform smoothing of historical time series and uncover periods of rapid change, though strong temporal autocorrelation can make it challenging to estimate key parameters (Camara et al, 2021; Knape, 2016; Simpson, 2018; Spooner et al, 2018; Yang et al, 2012).…”
Section: Introductionmentioning
confidence: 99%
“…Larger penalties shrink the coefficient covariances, effectively forcing the smooth toward a straight line when the data do not justify a nonlinear function (Marra and Wood 2011, Wood 2016). GAMs are particularly sought after for modelling time series to both identify nonlinear or time-varying covariate effects and to uncover periods of rapid change, though strong temporal autocorrelation can make it challenging to estimate key parameters (Yang et al 2012, Knape 2016, Simpson 2018, Spooner et al 2018, Camara et al 2021).…”
Section: Introductionmentioning
confidence: 99%