Conceição Lopes Costa
Integer-Valued APARCH Processes
In this work, an asymmetric power autoregressive conditional Poisson model is introduced for the analysis of time series of counts exhibiting asymmetric
overdispersion. Basic probabilistic and statistical properties of the model are discussed. Parameter estimation is also addressed: the conditional maximum likelihood (ML) estimation method is applied and the asymptotic properties of the conditional ML estimator are obtained. A simulation study is presented to illustrate the proposed model and the work finishes with an empirical application to a set of data concerning the daily number of stock transactions.
overdispersion. Basic probabilistic and statistical properties of the model are discussed. Parameter estimation is also addressed: the conditional maximum likelihood (ML) estimation method is applied and the asymptotic properties of the conditional ML estimator are obtained. A simulation study is presented to illustrate the proposed model and the work finishes with an empirical application to a set of data concerning the daily number of stock transactions.
Magda Monteiro
Discrimination of Water Quality Monitoring Sites in River Vouga using a Mixed-Effect State Space Model
The surface water quality monitoring is an important concern of public organizations due to its relevance for public health. Statistical methods are taken as consistent and essential tools in the monitoring procedures in order to prevent and identify environmental problems. This work presents the study case of the hydrological basin of the river Vouga, in Portugal. The main goal is to discriminate the water monitoring sites using the monthly dissolved oxygen concentration dataset between January 2002 and May 2013. This is achieved through the extraction of trend and seasonal components in a linear mixed-effect state space model. The parameters estimation is performed using distribution-free estimators in a two-step procedure. The application of the Kalman smoother algorithm allows to obtain predictions of the structural components as trend and seasonality. The water monitoring sites are discriminated through the structural components by a hierarchical agglomerative clustering procedure. This procedure identified different homogeneous groups relatively to the trend and seasonality components and some characteristics of the hydrological basin are presented in order to support the results.
