A Periodic State Space Model to Monthly Long-term Temperature Data

This work presents a periodic state space model to model monthly temperature data. Additionally, some issues are discussed, as the parameter estimation or the Kalman filter recursions adapted to a periodic model. This framework is applied to monthly long-term temperature time series of Lisbon

A note on prediction bias for state space models with estimated parameters

This paper aims to discuss some problems on state space models with estimated parameters. While existing research focus on the prediction mean squared error, this work presents some results on bias propagation into forecast and filter predictions when the mean vector of the state is taking with an estimation bias, namely, non recursive analytical expression for them. In particular, it is discussed the impact of mean bias in invariant state space models

Dynamic linear modeling of homogenized monthly temperature in Lisbon

This chapter focuses on the statistical modeling of the homogenized monthly average temperature data of Lisbon from 1856 to 2008. An exploratory analysis was performed using linear regression models which indicates the need of considering the temporal dependency and some flexibility in the trend modeling. In order to incorporate the properties of the data it was adopted a dynamic linear models with a fixed effect component. The model was fitted by a two-step procedure which combines the least squares method and the maximum likelihood estimation in the state space framework. The results indicated an average increase of the homogenized monthly temperature in Lisbon in about 0.427oC per century, between 1856 to 2008. Additionally, smoother predictions of the stochastic slopes indicated that the rise of temperature moderately changes according to the month, higher linear increases occurred in the winter months and lower increases occurred in the summer months

A Mixed-effect State Space Model to Environmental Data

This work presents some common issues in the statistical analysis of time series of environmental area. The discussion and the presentation of solutions is raised by the study of a time series of the oxygen concentration variable in a water quality monitoring site in the river Vouga hydrological basin in Portugal. Issues such as trends, seasonality, temporal correlation and detection of change points are addressed

A comparison between single site modeling and multiple site modeling approaches using Kalman filtering

This work presents a comparative study between two approaches to calibrate radar rainfall in real time. The weather radar provides continuous measurements in real-time which have errors of either meteorological or instrumental nature. Locally, gauge measurements have a greater performance than radar measurements that can be used to improve radar estimates. One way of doing that is via a state space representation associated to the Kalman filter algorithm. In the single- site modeling approach we use the linear calibration model applied in [1] and [3] while the multivariate state-space model proposed in [6] is used in the multiple site approach. This work aims to discuss and compare these two different state space formulations based on the same data set

Change point analysis in a state space framework to monthly temperature data in European cities

In this work, we present time series of monthly average temperatures in several Euro- pean locations which were statistically analyzed using a state space approach, where it is considered a model with a deterministic seasonal component and a stochastic trend. The analysis of smoother prediction of the stochastic trend and its comparison in a tem- poral viewpoint can reveal patterns about warming in Europe. The temperature rise rates in Europe seem to have increased in the last decades when compared with longer periods, hence a change point detection method is applied to the trend component in order to identify these possible changes in the monthly temperature rise rates. The adopted methodology pointed out, for most series a change point in the late eighties.publishe

Internationalization in health - target market: Mozambique: case study: clidis

This paper reflects a clear example of a successful internationalization strategy in the health sector. It also shows possible setbacks in developing countries. Clidis managed to overcome a lot of political, strategic and operational obstacles as their strategy, hard work and the quality of their services were rapidly noticed. Furthermore, their margin went from reflecting their very strong initial costs and low revenue, to showing their rapid success and growth in only a year

Dynamic fator Models for bivariate Count Data: an application to fire activity

The study of forest re activity, in its several aspects, is essencial to understand the phenomenon and to prevent environmental public catastrophes. In this context the analysis of monthly number of res along several years is one aspect to have into account in order to better comprehend this tematic. The goal of this work is to analyze the monthly number of forest res in the neighboring districts of Aveiro and Coimbra, Portugal, through dynamic factor models for bivariate count series. We use a bayesian approach, through MCMC methods, to estimate the model parameters as well as to estimate the common latent factor to both series

Kalman filtering approach in the calibration of radar rainfall data

This work presents a comparative study of some models to estimate radar rainfall in real time using the Kalman filtering approach. This comparison adresses the parameters estimation, the assessment of the accuracy estimates obtained by each model and the impact of the number of rain gauges used in the improvement of radar calibration estimates

Kalman filtering approach in the calibration of radar rainfall data: a comparative analysis of state space representations

In this chapter it is presented a comparative study of some methods to estimate radar rainfall in real time. This work in- tends to discuss and compare different state space formulations based on a same data set; for instance, the comparison between the mode- ling of the mean field radar rainfall logarithmic bias (Chumchean et al., 2006), a linear radar-rain gauge calibration model (Alpuim & Barbosa, 1999; Costa & Alpuim, 2011) and a power law model (Brown et al., 2001)