Estimation of unemployment rates in small areas of Portugal: a best linear unbiased prediction approach versus a hierarchical Bayes approach

The high level of unemployment is one of the major problems in most European countries nowadays. Hence, the demand for small area labor market statistics has rapidly increased over the past few years. The Labour Force Survey (LFS) conducted by the Portuguese Statistical Office is the main source of official statistics on the labour market at the macro level (e.g. NUTS2 and national level). However, the LFS was not designed to produce reliable statistics at the micro level (e.g. NUTS3, municipalities or further disaggregate level) due to small sample sizes. Consequently, traditional design-based estimators are not appropriate. A solution to this problem is to consider model-based estimators that "borrow information" from related areas or past samples by using auxiliary information. This paper reviews, under the model-based approach, Best Linear Unbiased Predictors and an estimator based on the posterior predictive distribution of a Hierarchical Bayesian model. The goal of this paper is to analyze the possibility to produce accurate unemployment rate statistics at micro level from the Portuguese LFS using these kinds of stimators. This paper discusses the advantages of using each approach and the viability of its implementation.

Prediction of the solar radiation using RBF neural networks and ground-to-sky images

In this study, Artificial Neural Networks are applied to multistep long term solar radiation prediction. The networks are trained as one-step-ahead predictors and iterated over time to obtain multi-step longer term predictions. Auto-regressive and Auto-regressive with exogenous inputs solar radiationmodels are compared, considering cloudiness indices as inputs in the latter case. These indices are obtained through pixel classification of ground-to-sky images. The input-output structure of the neural network models is selected using evolutionary computation methods.

A juvenile recruitment prediction model for Ruditapes decussatus (L.) (Bivalvia : Mollusca)

Despite the importance of larval abundance in determining the recruitment of benthic marine invertebrates and as a major factor in marine benthic community structure, relating planktonic larval abundance with post-settlement post-larvae and juveniles in the benthos is difficult. It is hampered by several methodological difficulties, including sampling frequency, ability to follow larval and post-larval or juvenile cohorts, and ability to calculate growth and mortality rates. In our work, an intensive sampling strategy was used. Larvae in the plankton were collected at weekly intervals, while post-larvae that settled into collectors were analysed fortnightly. Planktonic larval and benthic post-larval/juvenile cohorts were determined, and growth and mortality rates calculated. Integration of all equations allowed the development of a theoretical formulation that, based on the abundance and planktonic larval duration, permits an estimation of the future abundance of post-larvae/juveniles during the first year of benthic life. The model can be applied to a sample in which it was necessary only to measure larval length.

Acoustic-oceanographic data fusion for prediction of oceanic acoustic elds

Tese de doutoramento, Engenharia Electrónica e Telecomunicações (Processamento de Sinal), Faculdade de Ciências e Tecnologia, Universidade do Algarve, 2014