Name: Wanderson de Paula Pinto
Type: MSc dissertation
Publication date: 27/08/2013
Advisor:
Name |
Role |
|---|---|
| Valdério Anselmo Reisen | Advisor * |
Examining board:
| Name |
Role |
|---|---|
| Valdério Anselmo Reisen | Advisor * |
| Taciana Toledo de Almeida Albuquerque | Internal Examiner * |
| Manoel Raimundo de Sena Junior | External Examiner * |
Summary: Data of air pollution have generally missing observations. This research presents a study of
methods to estimate the autocorrelation function in the presence of missing data, based on the
work of Yajima and Nishino (1999). There is also some techniques for imputation of missing
data based on the use of the EM algorithm proposed by Dempster (1977), and the ARIMA time
series models of Box and Jenkins. Testing simulations with frame proportions of missing data
were performed to compare the mean square errors of the proposed estimators. The empirical
study showed that the proposed estimation method has good performance in terms of mean
squared error measures. As an illustration of the proposed methodology, two time series of concentrations
of Inhalable Particulate Matter (PM10) issued in the Region of Vit´oria, ES, Brazil,
are analyzed.
Keywords: PM10, autocorrelation function, missing data.
