This thesis develops models and methods for statistical analysis of presence-only data. Besides constructing new models, the emphasis is on the theoretical characteristics of new models and on Bayesian prediction. Monte Carlo Markov chains algorithms are developed for the new presence-only data models in order to be able to simulate the posterior distribution of the unknowns and the predictive distribution of variable of interest. The new methods are applied to simulated data. One application in ecologic science have been a driving force behind the work.

Bayesian Modeling of Presence-only Data

Natalia Golini
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2012-01-01

Abstract

This thesis develops models and methods for statistical analysis of presence-only data. Besides constructing new models, the emphasis is on the theoretical characteristics of new models and on Bayesian prediction. Monte Carlo Markov chains algorithms are developed for the new presence-only data models in order to be able to simulate the posterior distribution of the unknowns and the predictive distribution of variable of interest. The new methods are applied to simulated data. One application in ecologic science have been a driving force behind the work.
2012
https://iris.uniroma1.it/retrieve/handle/11573/917791/327699/PhD_Thesis_N_Golini.pdf
Bayesian models, Data augmentation, MCMC algorithm, Presence-only data, Potential distribution, Pseudo-absence approach, Semicontinuous data, Spatial statistics, Two-part model.
Natalia Golini
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1857838
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