https://graspa.org The official site of the Graspa SIS group Mon, 07 Jan 2019 10:20:39 +0000 en-US hourly 1 https://graspa.org/wp-content/uploads/2025/09/cropped-07-Graspa-verticale-ridotto-32x32.png https://graspa.org 32 32 https://graspa.org/my-page-32/ Mon, 07 Jan 2019 10:20:39 +0000 https://graspa.org/my-page-32/ Read More »Bayesian Modeling and MCMC Computation in Linear Logistic Regression for Presence-only Data]]> Presence-only data are referred to situations in which, given a censoring
mechanism, a binary response can be observed only with respect to on outcome,
usually called extit{presence}. In this work we present a Bayesian approach
to the problem of presence-only data based on a two levels scheme. A
probability law and a case-control design are combined to handle the double
source of uncertainty: one due to the censoring and one due to the sampling. We
propose a new formalization for the logistic model with presence-only data that
allows further insight into inferential issues related to the model. We
concentrate on the case of the linear logistic regression and, in order to make
inference on the parameters of interest, we present a Markov Chain Monte Carlo
algorithm with data augmentation that does not require the a priori knowledge
of the population prevalence. A simulation study concerning 24,000 simulated
datasets related to different scenarios is presented comparing our proposal to
optimal benchmarks.

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