Departament: MatemàtiquesLlibres, articles, comunicacions i altres documents elaborats per personal docent i investigador del Departamenthttp://hdl.handle.net/10234/70372024-03-29T15:57:01Z2024-03-29T15:57:01ZSecond Maximal Invariant Subgroups and Solubility of Finite Groupsshao, ChangguoBeltrán, Antoniohttp://hdl.handle.net/10234/2062222024-03-27T14:20:46Z2022-11-22T00:00:00ZSecond Maximal Invariant Subgroups and Solubility of Finite Groups
shao, Changguo; Beltrán, Antonio
Let G be a finite group and assume that a group of automorphisms A is acting on G such that A and G have coprime orders. We prove that the fact of imposing specific properties on the second maximal A-invariant subgroups of G determines that G is either soluble or isomorphic to a few non-soluble groups such as PSL(2, 5) or SL(2, 5).
2022-11-22T00:00:00ZA Poisson cokriging method for bivariate count dataPayares García, David EnriqueOsei, FrankMateu, JorgeStein, Alfredhttp://hdl.handle.net/10234/2061172024-03-02T02:03:46Z2023-01-01T00:00:00ZA Poisson cokriging method for bivariate count data
Payares García, David Enrique; Osei, Frank; Mateu, Jorge; Stein, Alfred
Bivariate spatially correlated count data appear naturally in several domains such as ecology, economy and epidemiology. Current methods for analysing such data lack simplicity, interpretability and computational awareness. This paper introduces Poisson cokriging, a bivariate geostatistical technique to model and predict spatially correlated count variables. Our method exploits classical geostatistical theory and the bivariate Poisson distribution to propose an adaptation of cokriging when the underlying process follows a bivariate Poisson structure. A simulation study and a real data application using counts from two mosquito-borne diseases in Colombia showed that our model successfully performs spatial predictions at unobserved locations under different settings. We demonstrate the competitive convenience of Poisson cokriging in filtering rates and modelling highly variant population sizes against traditional geostatistical methods. We conclude that Poisson cokriging improves prediction accuracy and reduces variance prediction errors in comparison with ordinary cokriging.
2023-01-01T00:00:00ZA local correlation integral method for outlier detection in spatially correlated functional dataSosa, JorgeMoraga, PaulaFlores, MIguelMateu, Jorgehttp://hdl.handle.net/10234/2061162024-03-01T16:32:48Z2023-01-01T00:00:00ZA local correlation integral method for outlier detection in spatially correlated functional data
Sosa, Jorge; Moraga, Paula; Flores, MIguel; Mateu, Jorge
This paper proposes a new methodology for detecting outliers in spatially correlated functional data. We use a Local Correlation Integral (LOCI) algorithm substituting the Euclidean distance calculation by the Hilbert space
distance weighted by the semivariogram, obtaining a weighted dissimilarity metric among the geo-referenced curves, which takes into account the spatial correlation structure. In addition, we also consider the distance proposed in Romano et al. (2020), which optimizes the distance calculation for spatially dependent functional data. A simulation study is conducted to evaluate the performance of the proposed methodology. We analyze the role of a threshold value appearing as an hyperparameter in our approach, and show that our distance weighted by the semivariogram is overall superior to the other types of distances considered in the study. We analyze time series of Land Surface Temperature (LST) data in the region of Andalusia (Spain), detecting significant outliers that would have not been detected using other procedures.
2023-01-01T00:00:00ZPoint process modeling through a mixture of homogeneous and self-exciting processesBriz-Redón, ÁlvaroMateu, Jorgehttp://hdl.handle.net/10234/2061152024-03-01T15:15:21Z2024-01-01T00:00:00ZPoint process modeling through a mixture of homogeneous and self-exciting processes
Briz-Redón, Álvaro; Mateu, Jorge
Self-exciting point processes allow modeling thetemporal location of an event of interest, consideringthe history provided by previously observed events.This family of point processes is commonly used inseveral areas such as criminology, economics, or seis-mology, among others. The standard formulation of theself-exciting process implies assuming that the under-lying stochastic process is dependent on its previoushistory over the entire period under analysis. In thispaper, we consider the possibility of modeling a pointpattern through a point process whose structure is notnecessarily of self-exciting type at every instant or tem-poral interval. Specifically, we propose a mixture pointprocess model that allows the point process to be eitherself-exciting or homogeneous Poisson, depending on theinstant within the study period. The performance of thismodel is evaluated both through a simulation study anda case study. The results indicate that the model is ableto detect the presence of instants in time, referred to aschange points, where the nature of the process varies.
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