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Poster Communications
SP1
Wednesday, September 4th
19:45
Additive mixed models with time series
structures
Caio Lucidius Naberenzy Azevedo
Unicamp
Valderio Anselmo Reisen Pascal Bondon Higor Cotta
Faradiba Sarquis
Many longitudinal studies are designed to investigate changes over time in a characteristic which is
measured repeatedly for each individual. In this formulation, the probability distribution for the
multiple measurements usually has the same form for each participant, but the parameters of that
distribution vary over individuals. In this context, the linear mixed model has been widely used
in epidemiologic studies quantifying the air pollutant effect on the health of the population with
a special attention to respiratory condition. Since, in general, the individuals are observed over
time, the variable measured repeatedly on a same subject is serially correlated. Therefore, time
correlation is one of the factors to be considered in the model. As in the standard regression
model with time series covariates, neglecting time correlation in the model estimation is likely
to cause bias in the parameter estimates. In this context, this paper investigate the effect of
time correlation in the additive mixed models with a special attention to the interaction between
the covariates variables such as, for example, temperature and pollutants. We suggest to use of
non-(semi-)parametric regression to mitigate this problem.
Keywords: Longitudinal data modeling; Additive mixed-models; Time-series models; Dependency
structure
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