Extreme value Birnbaum-Saunders regression models applied to environmental data

Extreme value models are widely used in different areas. The Birnbaum–Saunders distribution is receiving considerable attention due to its physical arguments and its good properties. We propose a methodology based on extreme value Birnbaum–Saunders regression models, which includes model formulation, estimation, inference and checking. We further conduct a simulation study for evaluating its performance. A statistical analysis with real-world extreme value environmental data using the methodology is provided as illustration.

Facilitative effects of bi-hemispheric tDCS in cognitive deficits of Parkinson Disease patients

Parkinson’s disease (PD) is a progressive neurodegenerative disorder, primarily characterized by motor symptoms such as tremor, rigidity, bradykinesia, stiffness, slowness and impaired equilibrium. Although the motor symptoms have been the focus in PD, slight cognitive deficits are commonly found in non-demented and non-depressed PD patients, even in early stages of the disease, which have been linked to the subsequent development of pathological dementia. Thus, strongly reducing the quality of life (QoL). Both levodopa therapy and deep brain stimulation (DBS) have yield controversial results concerning the cognitive symptoms amelioration in PD patients. That does not seems to be the case with transcranial direct current stimulation (tDCS), although better stimulation parameters are needed. Therefore we hypothesize that simultaneously delivering cathodal tDCS (or ctDCS), over the right prefrontal cortex delivered with anodal tDCS (or atDCS) to left prefrontal cortex could be potentially beneficial for PD patients, either by mechanisms of homeostatic plasticity and by increases in the extracellular dopamine levels over the striatum.

Predicting SO2 pollution incidents by means of additive models with optimum variable selection

The aim of this paper is to predict time series of SO2 concentrations emitted by coal-fired power stations in order to estimate in advance emission episodes and analyze the influence of some meteorological variables in the prediction. An emission episode is said to occur when the series of bi-hourly means of SO2 is greater than a specific level. For coal-fired power stations it is essential to predict emission epi- sodes sufficiently in advance so appropriate preventive measures can be taken. We proposed a meth- odology to predict SO2 emission episodes based on using an additive model and an algorithm for variable selection. The methodology was applied to the estimation of SO2 emissions registered in sampling lo- cations near a coal-fired power station located in Northern Spain. The results obtained indicate a good performance of the model considering only two terms of the time series and that the inclusion of the meteorological variables in the model is not significant.