Modeling human timing behavior

In order to understand human motor timing, individuals are instructed to synchronize their movements with repetitive environmental events. There are cognitive models accounting for the empirical findings obtained in such task. Cognitive models are usually formalized as system of equations that receives variables as input and predict output based on the input, the mathematical expression, and the parameters [1]. Because in experiments, there are always variables that can neither be manipulated nor controlled, i.e., there is noise within and beyond the Central Nervous System (CNS), these model are often defined as parametric family of probability distributions. Schulze and Vorberg (2002) developed such a probabilistic cognitive model, called the Linear Phase Correction model (LPC). Our main goal is to provide a method of parameter estimation of the LPC built on multiple short asynchrony series that can be non-stationary, vary in size, and allow for serially correlated errors.Sociedade Portuguesa de Estatística (SPE)info:eu-repo/semantics/publishedVersio

Geostatistical analysis under preferential sampling

In geostatistics it is commonly assumed that the selection of the sampling locations does not depend on the values of the spatial variable. One has preferential sampling when this assumption fails (e.g. maximum values search). We first show that the impact of a preferential design on the traditional prediction methods is not negligible. We address this problem by proposing a model-based approach, for stationary Gaussian processes. This new parametric model is founded on a flexible class of log-Gaussian Cox processes. A numerical study is then included to compare the performance of the model proposed and the traditional geostatistical model

Modelação de séries temporais longas de variáveis hidrológicas. O caso do rio Danúbio

Este estudo desenrolou-se no âmbito de uma bolsa FCT de Iniciação à Investigação (BIC) do Programa Estratégico UID/MAT/00013/2013, tendo como principal objetivo a modelação e identificação de tendências em séries temporais longas, focando-se no caso de variáveis hidrológicas observadas no rio Danúbio. Os dados analisados dizem respeito a valores de descargas de água, recolhidos diariamente e anualmente, entre o ano de 1931 e o ano de 1990, em três locais distintos: Achleiten, Bratislava e Viena. Os dados recolhidos diariamente são relativos a médias diárias, por sua vez os dados recolhidos anualmente são relativos a máximos anuais da descarga de água. Para além de serem analisadas séries temporais diárias e anuais, foram tidas em conta séries temporais resultantes de agregação mensal e trimestral. Os principais métodos de análise adotados incluíram a análise das funções empíricas de auto-correlação total e parcial, funções de correlação cruzada, tendo-se posteriormente recorrido à modelação SARIMA e ARMAX, e a métodos de análise de tendência.Sociedade Portuguesa de Estatística financiou participação no congresso de um dos autores.info:eu-repo/semantics/publishedVersio

Nonparametric approaches for estimating risk maps

Assessment of environmental contamination is increasingly a concern in nowadays soci- ety. The maximum levels for pollutants are heavily regulated, being necessary to ensure compliance. Consequently, it becomes important to construct probability maps of the observation region, showing the complementary value of the distribution function of the variable involved at regulatory thresholds. These are usually called risk maps in the environmental setting. In this work, two kernel-type estimators of the spatial distribution function are constructed, which de- part from approximating the distribution at the sampled sites and then obtaining a weighted average of the resulting values, to derive a valid estimator at any random location. Consistency of both ap- proaches is proved under rather general conditions, such as local stationarity and the existence of a number of derivatives of the distribution function. Unlike other alternatives, the new proposals pro- vide non-decreasing functions and do not require a previous estimation of the indicator variogram or the trend function. However, appropriate bandwidths parameters are needed and selection of them in practice needs to be addressed. Numerical studies are carried out, aiming at comparing the current proposal with more usual methods, such as those based on the sill estimation or the indicator kriging, described in Journel (1983) or Goovaerts (1997), respectively, and redesigned in García-Soidán and Menezes (2012). Finally, the new proposal is applied to arsenic data from Portugal, so that pollution risk maps of the referred region are constructed. Moreover, accuracy maps of the probability estimates might be constructed based on bootstrap replicas, as described in García-Soidán, Menezes and Rubiños (2014).info:eu-repo/semantics/publishedVersio

Spatio-temporal stochastic modelling (METMAVI)

Editorial letter for the Special Issue dedicated to the VI International Workshop on Spatio-temporal Modelling (METMAVI), which took place in Guimarães-Portugal from 12 to 14 September 2012. This SI summarizes the main contributions made at METMAVI, related to spatio-temporal methodology illustrated with environmental applications

Modelling environmental monitoring data coming from different surveys

Environmental monitoring networks are providing large amounts of spatio-temporal data. Air pollution data, as other environmental data, exhibit a spatial and a temporal correlated nature. To improve the accuracy of predictions at unmonitored locations, there is a growing need for models capturing those spatio-temporal correlations. With this work, we propose a spatio-temporal model for gaussian data collected in a few number of surveys. We assume the spatial correlation structure to be the same in all surveys. In an application of this model to real data, concerning heavy metal concentrations in mosses collected from three surveys occurring between 1992 and 2002 in mainland Portugal, the data set is dense in the spatial dimension but sparse in the temporal one, thus our model-based approach corresponds to a saturated correlation model in the time dimension. A novel interpretation for the space-time covariance function is introduced. A simulation study, aiming to validate the model, provided better results in terms of accuracy with the novel covariance function. Prediction maps of the observed variable for the most recent survey, and of the inter- polation error as a measure of accuracy, are presented.The authors thank the Centre of Environmental Biology of Lisbon University for permission to use the moss data. The authors acknowledge financial support from the Portuguese Funds through FCT (Fundacao para a Ciencia e a Tecnologia), within the Project UID/MAT/00013/2013.info:eu-repo/semantics/publishedVersio

Modelling environmental monitoring data coming from different surveys

With this work we propose a spatio-temporal model for Gaussian data collected in a small number of surveys. We assume the spatial correlation structure to be the same in all surveys. In the application concerning heavy metal concentrations in mosses, the data set is dense in the spatial dimension but sparse in the temporal one, thus our model-based approach corresponds to a correlation model depending on survey orders. One advantage of this approach is its computational simplicity. An interpretation for the space-time covariance function, decomposing the overall variance of the process as the product of the spatial component variance by the temporal component variance, is introduced. A simulation study, aiming to validate the model, provided better results in terms of accuracy with the novel covariance function. Maps of predicted heavy metal concentrations and of interpolation error, for the most recent survey, are presented.Data of this kind is recurrent in environmental sciences, which is why we argue that this will be a practical tool to be used very often

Spatio‐temporal analysis of land use/land cover change dynamics in Paraguai/Jauquara Basin, Brazil

Data was collected from freely available images composites from the catalogs of the United States Geological Survey.Although global climate change is receiving considerable attention, the loss of biodiversity worldwide continues. In this study, dynamics of land use/land cover (LULC) change in the Paraguai/Jauquara Basin, Mato Grosso, Brazil, were investigated. Two analyses were performed using R software. The first was a comparative study of LULC among the LULC classes at the polygon scale, and the second was a spatio-temporal analysis of moving polygons restricted to the agricultural regions in terms of topology, size, distance, and direction of change. The data consisted of Landsat images captured in 1993, 1997, 2001, 2005, 2009, 2013, and 2016 and processed using ArcGIS software. The proposed analytical approach handled complex data structures and allowed for a deeper understanding of LULC change over time. The results showed that there was a statistically significant change from regions of natural vegetation to pastures, agricultural regions, and land for other uses, accompanied by a significant trend of expansion of agricultural regions, appearing to stabilize from 2005. Furthermore, different patterns of LULC change were found according to soil type and elevation. In particular, the purple latosol soil type presented the highest expansion indexes since 2001, and the elevated agricultural areas have been expanding and/or stabilizing since 1997.This work is part of the results of the research projects PTDC/MAT-STA/28243/2017 funded by the FCT (Fundação para a Ciência e Tecnologia) and Analise temporal do uso da terra para definição de cenários de mudança da paisagem natural por intervenções de natureza humana no Pantanal de Caceres/MT funded by Fundação de Amparo a Pesquisa do Estado de Mato Grosso-FAPEMAT. The first author also acknowledges Foundation FCT (Fundação para a Ciência e Tecnologia) for funding this research through Individual Scholarship Ph.D. PD/BD/150535/2019

Entry games for the airline industry

In this paper we review the literature on static entry games and show how they can be used to estimate the market structure of the airline industry. The econometrics challenges are presented, in particular the problem of multiple equilibria and some solutions used in the literature are exposed. We also show how these models, either in the complete information setting or in the incomplete information one, can be estimated from i.i.d. data on market presence and market Characteristics. We illustrate it by estimating a static entry game with heterogeneous firms by Simulated Maximum Likelihood on European data for the year 2015