Search results for " 62M30"

showing 4 items of 4 documents

Hierarchical log Gaussian Cox process for regeneration in uneven-aged forests

2021

We propose a hierarchical log Gaussian Cox process (LGCP) for point patterns, where a set of points x affects another set of points y but not vice versa. We use the model to investigate the effect of large trees to the locations of seedlings. In the model, every point in x has a parametric influence kernel or signal, which together form an influence field. Conditionally on the parameters, the influence field acts as a spatial covariate in the intensity of the model, and the intensity itself is a non-linear function of the parameters. Points outside the observation window may affect the influence field inside the window. We propose an edge correction to account for this missing data. The par…

0106 biological sciencesStatistics and ProbabilityFOS: Computer and information sciences62F15 (Primary) 62M30 60G55 (Secondary)MCMCGaussianBayesian inferenceMarkovin ketjutStatistics - Applications010603 evolutionary biology01 natural sciencesCox processMethodology (stat.ME)010104 statistics & probabilitysymbols.namesakeregeneraatio (biologia)Applied mathematicsApplications (stat.AP)0101 mathematicsLaplace approximationStatistics - MethodologyGeneral Environmental ScienceParametric statisticsMathematicsspatial random effectsbayesilainen menetelmäMarkov chain Monte CarloFunction (mathematics)15. Life on landMissing dataMonte Carlo -menetelmätcompetition kernelLaplace's methodKernel (statistics)symbolstree regenerationpuustometsänhoitomatemaattiset mallitStatistics Probability and Uncertainty
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Bayesian hierarchical models for analysing the spatial distribution of bioclimatic indices

2017

A methodological approach for modelling the spatial distribution of bioclimatic indices is proposed in this paper. The value of the bioclimatic index is modelled with a hierarchical Bayesian model that incorporates both structured and unstructured random effects. Selection of prior distributions is also discussed in order to better incorporate any possible prior knowledge about the parameters that could refer to the particular characteristics of bioclimatic indices. MCMC methods and distributed programming are used to obtain an approximation of the posterior distribution of the parameters and also the posterior predictive distribution of the indices. One main outcome of the proposal is the …

Bioclimatologia:62 Statistics::62M Inference from stochastic processes [Classificació AMS]BioclimatologyBioclimatology geostatistics parallel computation spatial prediction:62 Statistics::62P Applications [Classificació AMS]62F15 62M30 62P10 62P12 86A32Estadística bayesiana:Matemàtiques i estadística::Estadística matemàtica [Àrees temàtiques de la UPC]spatial prediction:62 Statistics::62F Parametric inference [Classificació AMS]geostatistics:86 Geophysics [Classificació AMS]parallel computation
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A multi-scale area-interaction model for spatio-temporal point patterns

2018

Models for fitting spatio-temporal point processes should incorporate spatio-temporal inhomogeneity and allow for different types of interaction between points (clustering or regularity). This paper proposes an extension of the spatial multi-scale area-interaction model to a spatio-temporal framework. This model allows for interaction between points at different spatio-temporal scales and the inclusion of covariates. We fit the proposed model to varicella cases registered during 2013 in Valencia, Spain. The fitted model indicates small scale clustering and regularity for higher spatio-temporal scales.

FOS: Computer and information sciencesStatistics and ProbabilityScale (ratio)Computer scienceManagement Monitoring Policy and LawMulti-scale area-interaction modelcomputer.software_genreVaricella01 natural sciencesPoint processMethodology (stat.ME)010104 statistics & probability0502 economics and businessStatisticsCovariate60D05 60G55 62M30Point (geometry)0101 mathematicsComputers in Earth SciencesCluster analysisStatistics - Methodology050205 econometrics 05 social sciencesInteraction modelExtension (predicate logic)Gibbs point processesComputingMethodologies_PATTERNRECOGNITIONSpatio-temporal point processesData miningcomputer
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The second-order analysis of marked spatio-temporal point processes, with an application to earthquake data

2016

To analyse interaction in marked spatio-temporal point processes (MSTPPs), we introduce marked (cross) second-order reduced moment measures and K-functions for general inhomogeneous second-order intensity reweighted stationary MSTPPs. These summary statistics, which allow us to quantify dependence between different mark categories of the points, are depending on the specific mark space and mark reference measure chosen. We also look closer at how the summary statistics reduce under assumptions such as the MSTPP being multivariate and/or stationary. A new test for independent marking is devised and unbiased minus-sampling estimators are derived for all statistics considered. In addition, we …

Methodology (stat.ME)FOS: Computer and information sciences60G55 60D05 62M30Statistics - Methodology
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