1,721,053 research outputs found
University student talent: the real driver for performance?
Full text linkInvestigation about the university student performance, and its measurement, are
very crucial issues for any policy maker. Since the economic crisis, jobs market requires even
higher skills and competences. Literature offers a lot of papers about the university student
quality and performance, in order to identify the main determinants of them. Often, results are
very different, and they seems to hold just in a specific context. This paper aims to investigate
the role of a latent variable that can take into account the student motivation, aptitude, and
abilities, here conveniently called talent. A random effect Quantile Regression on a new measure
of Italian student performance has been adopted, and results seem to highlight the main role
of the talent
A space-time branching process with covariates
Full text linkThe paper proposes a stochastic process that improves the assessment of seismic events in
space and time, considering a contagion model (branching process) within a regression-like framework.
The proposed approach develops the Forward Likelihood for prediction (FLP) method including
covariates in the epidemic component
Space-time Point Processes semi-parametric estimation with predictive measure information
Full text linkIn this paper, we provide a method to estimate the space-time intensity of a branching-type point process by mixing
nonparametric and parametric approaches.
The method accounts simultaneously for the
estimation of the different model components, applying a
forward predictive likelihood estimation approach to
semi-parametric models
Multiscale processes to describe the Eastern Sicily Seismic Sequences
Full text linkIn this paper, a version of hybrid of Gibbs point process models is proposed as method to characterise the multiscale interaction structure of several seismic sequences occurred in the Eastern Sicily in the last decade. Seismic sequences were identified by a clustering technique based on space-time distance criterion and hierarchical clustering. We focus our analysis on five small seismic sequences, showing that two of these are described by an inhomogeneous Poisson process (not significant interaction among events) while the other three clusters are described by a hybrid-Geyer process (mutiscale interaction between events). The proposed method, although it still needs extensive testing on a larger catalogue, seems to be a promising tool for the characterization of seismogenic sources through the analysis of induced seismicity
Convective and stratiform precipitation: A PCA-based clustering algorithm for their identification
Full text linkThe increasing occurrence of flood events in some areas of the Southern Mediterranean area (e.g.,
Sicily), over the last few years, has contributed to raising the importance of characterizing such
events and identifying their causes
Correction to: Local spatial log-Gaussian Cox processes for seismic data (AStA Advances in Statistical Analysis, (2022), 10.1007/s10182-022-00444-w)
No full textIn this article, Figs. 1a, 2a-c, 9 and 11 should have appeared as shown below. The original article has been corrected
Some properties of local weighted second-order statistics for spatio-temporal point processes
Full text linkDiagnostics of goodness-of-fit in the theory of point processes are often considered through the transformation of data into residuals as a result of a thinning or a rescaling procedure. We alternatively consider here second-order statistics coming from weighted measures. Motivated by Adelfio and Schoenberg (2009) for the temporal and spatial cases, we consider an extension to the spatio-temporal context in addition to focussing on local characteristics. In particular, our proposed method assesses goodness-of-fit of spatio-temporal models by using local weighted secondorder statistics, computed after weighting the contribution of each observed point by the inverse of the conditional intensity function that identifies the process. Weighted second-order statistics directly apply to data without assuming homogeneity nor transforming the data into residuals, eliminating thus the sampling variability due to the use of a transforming procedure. We provide some characterisations and show a number of simulation studies
Spatio-temporal classification in point patterns under the presence of clutter
Full text linkWe consider the problem of detection of features in the presence of clutter for spatio-temporal point patterns. In previous studies, related to the spatial context, Kth nearest-neighbor distances to classify points between clutter and features. In particular, a mixture of distributions whose parameters were estimated using an expectation-maximization algorithm. This paper extends this methodology to the spatio-temporal context by considering the properties of the spatio-temporal Kth nearest-neighbor distances. For this purpose, we make use of a couple of spatio-temporal distances, which are based on the Euclidean and the maximum norms. We show close forms for the probability distributions of such Kth nearest-neighbor distances and present an intensive simulation study together with an application to earthquakes
Hydrological post-processing based on approximate Bayesian computation (ABC)
No full textThis study introduces a method to quantify the conditional predictive uncertainty in hydrological post-processing contexts when it is cumbersome to calculate the likelihood (intractable likelihood). Sometimes, it can be difficult to calculate the likelihood itself in hydrological modelling, specially working with complex models or with ungauged catchments. Therefore, we propose the ABC post-processor that exchanges the requirement of calculating the likelihood function by the use of some sufficient summary statistics and synthetic datasets. The aim is to show that the conditional predictive distribution is qualitatively similar produced by the exact predictive (MCMC post-processor) or the approximate predictive (ABC post-processor). We also use MCMC post-processor as a benchmark to make results more comparable with the proposed method. We test the ABC post-processor in two scenarios: (1) the Aipe catchment with tropical climate and a spatially-lumped hydrological model (Colombia) and (2) the Oria catchment with oceanic climate and a spatially-distributed hydrological model (Spain). The main finding of the study is that the approximate (ABC post-processor) conditional predictive uncertainty is almost equivalent to the exact predictive (MCMC post-processor) in both scenarios
Approximate Bayesian Computation for Forecasting in Hydrological models
Full text linkApproximate Bayesian Computation (ABC) is a statistical tool for handling
parameter inference in a range of challenging statistical problems, mostly
characterized by an intractable likelihood function. In this paper, we focus on the
application of ABC to hydrological models, not as a tool for parametric inference,
but as a mechanism for generating probabilistic forecasts. This mechanism is referred
as Approximate Bayesian Forecasting (ABF). The abcd water balance model
is applied to a case study on Aipe river basin in Columbia to demonstrate the applicability
of ABF. The predictivity of the ABF is compared with the predictivity of the
MCMC algorithm. The results show that the ABF method as similar performance
as the MCMC algorithm in terms of forecasting. Despite the latter is a very flexible
tool and it usually gives better parameter estimates it needs a tractable likelihoo
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