9 citations · 14 across the 10 of their papers we have counts for
11 papers
False Positives, False Negatives, and the Detection-Only Problem: A Hierarchical Model for Species Occurrence with Observation Error
Kabiru Abubakari, Eleni Matechou, Marie C. Henniges +3
Monitoring species occurrence is essential for understanding biodiversity change, informing conservation decisions, and assessing the impact of environmental pressures on ecosystem…
Evaluating the Cauchy Combination Test for Count Data
Huda Alsulami, Silvia Liverani
The Cauchy combination test (CCT) is a -value combination method used in multiple-hypothesis testing and is robust under dependence structures. This study aims to evaluate the C…
Beyond Conjugacy for Chain Event Graph Model Selection
Aditi Shenvi, Silvia Liverani
Chain event graphs are a family of probabilistic graphical models that generalise Bayesian networks and have been successfully applied to a wide range of domains. Unlike Bayesian n…
Structure induced by a multiple membership transformation on the Conditional Autoregressive model
Marco Gramatica, Silvia Liverani, Peter Congdon
The objective of disease mapping is to model data aggregated at the areal level. In some contexts, however, (e.g. residential histories, general practitioner catchment areas) when…
Variance matrix priors for Dirichlet process mixture models with Gaussian kernels
Wei Jing, Michail Papathomas, Silvia Liverani
The Dirichlet Process Mixture Model (DPMM) is a Bayesian non-parametric approach widely used for density estimation and clustering. In this manuscript, we study the choice of prior…
Bayesian modelling for spatially misaligned health areal data: a multiple membership approach
Marco Gramatica, Peter Congdon, Silvia Liverani
Diabetes prevalence is on the rise in the UK, and for public health strategy, estimation of relative disease risk and subsequent mapping is important. We consider an application to…
Dirichlet Process Mixture Models for Regression Discontinuity Designs
Federico Ricciardi, Silvia Liverani, Gianluca Baio
The Regression Discontinuity Design (RDD) is a quasi-experimental design that estimates the causal effect of a treatment when its assignment is defined by a threshold value for a c…
Predicting success in the worldwide start-up network
Moreno Bonaventura, Valerio Ciotti, Pietro Panzarasa +3
By drawing on large-scale online data we construct and analyze the time-varying worldwide network of professional relationships among start-ups. The nodes of this network represent…
Modelling collinear and spatially correlated data
Silvia Liverani, Aurore Lavigne, Marta Blangiardo
In this work we present a statistical approach to distinguish and interpret the complex relationship between several predictors and a response variable at the small area level, in…
Sampling from Dirichlet process mixture models with unknown concentration parameter: Mixing issues in large data implementations
David I. Hastie, Silvia Liverani, Sylvia Richardson
We consider the question of Markov chain Monte Carlo sampling from a general stick-breaking Dirichlet process mixture model, with concentration parameter alpha. This paper introduc…
PReMiuM: An R Package for Profile Regression Mixture Models using Dirichlet Processes
Silvia Liverani, David I. Hastie, Lamiae Azizi +2
PReMiuM is a recently developed R package for Bayesian clustering using a Dirichlet process mixture model. This model is an alternative to regression models, non-parametrically lin…