Abstract
Many time series encountered in practice are nonstationary, and instead are often generated from a process with a unit root. Because of the process of data collection or the practice of researchers, time series used in analysis and modeling are frequently obtained through temporal aggregation. As a result, the series used in testing for a unit root are often time series aggregates. In this paper, we study the effects of the use of aggregate time series on the Dickey-Fuller test for a unit root. We start by deriving a proper model for the aggregate series. Based on this model, we find the limiting distributions of the test statistics and illustrate how the tests are affected by the use of aggregate time series. The results show that those distributions shift to the right and that this effect increases with the order of aggregation, causing a strong impact both on the empirical significance level and on the power of the test. To correct this problem, we present tables of critical points appropriate for the tests based on aggregate time series and demonstrate their adequacy. Examples illustrate the conclusions of our analysis.

Abstract
We have been working on a model for the shallow-layer settler, in a mixer-settler system, which is able to describe the hydrodynamic phenomena of the transient state of a liquid-liquid system. The mathematical model used includes parameters of the drop transport process as well as of the drop-drop and drop-interface coalescence with the active interface. The most adequate values of these parameters are unknown. In order to tune the model parameters we have linked the mixer-settler simulation algorithm to an optimization procedure. Due to its simplicity and robustness we've used the Hooke-Jeeves optimization algorithm to fit these parameters to given experimental results. © 2008 Civil-Comp Press.

Abstract
This work considers a generalization of the INAR(1) model to the panel data first order Seemingly Unrelated INteger AutoRegressive Poisson model, SUINAR(1). It presents Bayesian and classical methodologies to estimate the parameters of Poisson SUINAR(1) model and to forecast future observations of the process. In particular, prediction intervals for forecasts - classical approach - and HPD prediction intervals - Bayesian approach - are derived. A simulation study is provided to give additional insight into the finite sample behaviour of the parameter estimates and forecasts.

Abstract
The characterisation and quantification of long-term sea-level variability is of considerable interest in a climate change context. Long time series from coastal tide gauges are particularly appropriate for this purpose. Long-term variability in tide gauge records is usually expressed through the linear slope resulting from the fit of a linear model to the time series, thus assuming that the generating process is deterministic with a short memory component. However, this assumption needs to be tested, since trend features can also be due to non-deterministic processes such as random walk or long range dependent processes, or even be driven by a combination of deterministic and stochastic processes. Specific methodology is therefore required to distinguish between a deterministic trend and stochastically-driven trend-like features in a time series. In this chapter, long-term sea-level variability is characterised through the application of (i) parametric statistical tests for stationarity, (ii) wavelet analysis for assessing scaling features, and (iii) generalised least squares for estimating deterministic trends. The results presented here for long tide gauge records in the North Atlantic show, despite some local coherency, profound differences in terms of the low frequency structure of these sea-level time series. These differences suggest that the long-term variations are reflecting mainly local/regional phenomena. © 2008 Springer-Verlag Berlin Heidelberg.

Abstract
Sea level is a key variable in the context of global climate change. Climate-induced variability is expected to affect not only the mean sea level but also the amplitude and phase of its seasonal cycle. This study addresses the changes in the amplitude and phase of the annual cycle of coastal sea level in the extra-tropical North Atlantic. The physical causes of these variations are explored by analysing the association between fluctuations in the annual amplitude of sea level and in ancillary parameters [atmospheric pressure, sea-surface temperature and North Atlantic Oscillation (NAO) winter index]. The annual cycle is extracted through autoregressive decomposition, in order to be able to separate variations in seasonality from long-term interannual variations in the mean. The changes detected in the annual sea level cycle are regionally coherent, and related to changes in the analysed forcing parameters. At the northern sites, fluctuations in the annual amplitude of sea level are associated with concurrent changes in temperature, while atmospheric pressure is the dominant influence for most of the sites on the western boundary. The state of the NAO influences the annual variability in the Southern Bight, possibly through NAO-related changes in wind stress and ocean circulation.

Abstract
We study a new method for improving the classification accuracy of a model composed of classification association rules (CAR). The method consists in reordering the original set of rules according to the error rates obtained on a set of training examples. This is done iteratively, starting from the original set of rules. After obtaining N models these are used as an ensemble for classifying new cases. The net effect of this approach is that the original rule model is clearly improved. This improvement is due to the ensembling of the obtained models, which are, individually, slightly better than the original one. This ensembling approach has the advantage of running a single learning process, since the models in the ensemble are obtained by self replicating the original one.