24 citations · 42 across the 9 of their papers we have counts for
10 papers · 1 filter
A Standardization Procedure to Incorporate Variance Partitioning Based Priors in Latent Gaussian Models
Luisa Ferrari, Massimo Ventrucci
Latent Gaussian Models (LGMs) are a subset of Bayesian Hierarchical models where Gaussian priors, conditional on variance parameters, are assigned to all effects in the model. LGMs…
Informed Bayesian Finite Mixture Models via Asymmetric Dirichlet Priors
Garritt L. Page, Massimo Ventrucci, Maria Franco-Villoria
Finite mixture models are flexible methods that are commonly used for model-based clustering. A recent focus in the model-based clustering literature is to highlight the difference…
A comparison of priors for variance parameters in Bayesian basket trials
Massimo Ventrucci, Alessandro Vagheggini
Phase II basket trials are popular tools to evaluate efficacy of a new treatment targeting genetic alteration common to a set of different cancer histologies. Efficient designs are…
Variance partitioning in spatio-temporal disease mapping models
Maria Franco-Villoria, Massimo Ventrucci, Håvard Rue
Bayesian disease mapping, yet if undeniably useful to describe variation in risk over time and space, comes with the hurdle of prior elicitation on hard-to-interpret random effect…
A spectral adjustment for spatial confounding
Yawen Guan, Garritt L. Page, Brian J Reich +2
Adjusting for an unmeasured confounder is generally an intractable problem, but in the spatial setting it may be possible under certain conditions. In this paper, we derive necessa…
PC priors for residual correlation parameters in one-factor mixed models
Massimo Ventrucci, Daniela Cocchi, Gemma Burgazzi +1
Lack of independence in the residuals from linear regression motivates the use of random effect models in many applied fields. We start from the one-way anova model and extend it t…
A unified view on Bayesian varying coefficient models
Maria Franco-Villoria, Massimo Ventrucci, Håvard Rue
Varying coefficient models are useful in applications where the effect of the covariate might depend on some other covariate such as time or location. Various applications of these…
P-spline smoothing for spatial data collected worldwide
Fedele Greco, Massimo Ventrucci, Elisa Castelli
Spatial data collected worldwide at a huge number of locations are frequently used in environmental and climate studies. Spatial modelling for this type of data presents both metho…
A note on intrinsic Conditional Autoregressive models for disconnected graphs
Anna Freni-Sterrantino, Massimo Ventrucci, Håvard Rue
In this note we discuss (Gaussian) intrinsic conditional autoregressive (CAR) models for disconnected graphs, with the aim of providing practical guidelines for how these models sh…
Penalized complexity priors for degrees of freedom in Bayesian P-splines
Massimo Ventrucci, Håvard Rue
Bayesian P-splines assume an intrinsic Gaussian Markov random field prior on the spline coefficients, conditional on a precision hyper-parameter . Prior elicitation of is di…