1,721,053 research outputs found
Hints of latent drivers investigating university student performance
Full text linkJob market, nowadays, asks for higher and higher skills and competences. Therefore, also the measurement and assessment of the university students performance are crucial issues for policy makers. Although the scientific literature provides several papers investigating the main determinants of university student performance, often results are very different, and they seem to hold just in very specific contexts. This paper aims to contribute to the international literature, focusing on the role of student specific characteristics, supporting the idea that unobservable variables (such as motivation, aptitudes or abilities) should be more investigated
Minimum contrast for the first-order intensity estimation of spatial and spatio-temporal point processes
Full text linkIn this paper, we harness a result in point process theory, specifically the expectation of the weighted K-function, where the weighting is done by the true first-order intensity function. This theoretical result can be employed as an estimation method to derive parameter estimates for a particular model assumed for the data. The underlying motivation is to avoid the difficulties associated with dealing with complex likelihoods in point process models and their maximization. The exploited result makes our method theoretically applicable to any model specification. In this paper, we restrict our study to Poisson models, whose likelihood represents the base for many more complex point process models. In this context, our proposed method can estimate the vector of local parameters that correspond to the points within the analyzed point pattern without introducing any additional complexity compared to the global estimation. We illustrate the method through simulation studies for both purely spatial and spatio-temporal
point processes, and show complex scenarios based on the Poisson model through
the analysis of two real datasets concerning environmental problems
Minimum contrast for estimating point processes intensity
No full textA result in point process theory, based on the expectation of the weighted K-function, is exploited by the true first-order intensity function. This
theoretical result can be an estimation method for obtaining the parameter estimates of a specific model assumed for the data. The motivation is
to avoid dealing with the complex likelihoods of some complex point process models and their maximization. This can be more evident when
considering the local second-order characteristics since the proposed method can estimate the vector of the local parameters corresponding to the
points of the analysed point pattern. We illustrate the method through simulation studies for purely spatial and spatio-temporal point processes
Selecting the Kth nearest-neighbour for clutter removal in spatial point processes through segmented regression models
No full textWe consider the problem of feature detection, in the presence of clutter in spatial point processes. A previous study addresses the issue of the selection of the best nearest neighbour for clutter removal. We outline a simple workflow to automatically estimate the number of nearest neighbours by means of segmented regression models applied to an entropy measure of cluster separation. The method is suitable for a feature with clutter as two superimposed Poisson processes on any twodimensional space, including linear networks. We present simulations to illustrate the method and an application to the problem of seismic fault detection
Minimum contrast for point processes' first-order intensity estimation
No full textIn this paper, we exploit some theoretical results, from which we know the expected value of the K-function weighted by the true first-order intensity function of a point pattern. This theoretical result can serve as an estimation method for obtaining the parameter estimates of a specific model, assumed for the data. The only requirement is the knowledge of the first-order intensity function expression, completely avoiding writing the likelihood, which is often complex to deal with in point process models. We illustrate the method through simulation studies for spatio-temporal point processes
Local Spatio-Temporal Log-Gaussian Cox Processes for seismic data analysis
No full textWe propose a local version of the spatio-temporal log-Gaussian Cox processes (LGCPs) employing the Local Indicators of Spatio-Temporal Association (LISTA) functions into the minimum contrast procedure to obtain space as well as time-varying parameters.
We resort to the joint minimum contrast method fitting method to estimate the set of second-order parameters for the class of Spatio-Temporal LGCPs.
We employ the proposed methodology to analyse real seismic data occurred Greece between 2004 and 2015
Seismic events classification through latent class regression models for point processes
No full textWe are trying to identify sub-processes of seismic events from the point processes’ point of view and according to the latent class regression approach. Each seismic event is classified as membership of one of the 4 identified sub-classes of seismic sequences, each defined by particular and well-defined characteristics. So far, seismic sub-sequences have been identified and described according to several declustering methods. In this application, we show how sub-processes can be identified starting from the definition of a spatio-temporal intensity function for point processes, assuming independence of the past
Comparing local structures of spatio-temporal point processes on linear networks
No full textWe employ the Local Indicators of Spatio-Temporal Association (LISTA) functions on linear networks to build a statistical test for local second-order structure. This allows to identify differences in the spatio-temporal clustering behaviour of two point patterns, a point pattern of interest and a background one, both occurring on the same linear network. We illustrate the proposed methodology analysing a traffic-related problem
Severe storms events’ reproduction in the United States of America: Evaluation from the marked self-exciting point processes point of view
No full textThe proposed application focuses on the evaluation of hailstorms’ and thunderstorms winds’ events in the United States of America, in the period from 1996 to 2022, by a marked spatio-temporal self-exciting point process estimation. The aim of this application is the assessment and explanation of the spatio-temporal spontaneous and reproducing activity of severe hailstorms’ and thunderstorms winds’ processes. Though the spatio-temporal dynamics of the underlying spatio-temporal process are not exactly evaluable according to the self-exciting processes’ theoretical framework, the present application allows us to demonstrate how the spatio-temporal pattern is well-fitted and clearly explainable, according to the flexible semi-parametric ETAS-FLP approach
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