arXiv subjects

Oswaldo Gressani

Publications and source records attributed to Oswaldo Gressani.

8 recordsLinked to original sources

arXiv2026

A Time-Series Model for Areal Data Using Area-Specific Gaussian Processes with Spatially Correlated Hyperparameters

In many applied settings, areal data are observed repeatedly over long time periods, as commonly occurs in infectious disease surveillance and environmental or demographic monitoring. Accurate characterization of local temporal dynamics and uncertainty is important for monitoring disease trends, identifying local changes, and supporting public health decision-making. Traditional spatio-temporal models generally represent spatial, temporal, and space-time interaction components through structured random effects acting on the latent outcome process. We propose a Bayesian spatio-temporal hierarchical framework in which temporal dynamics are modeled using area-specific Gaussian processes, while spatial dependence is introduced through spatially correlated Gaussian-process covariance hyperparameters. This allows neighboring regions to share information about the characteristics of their temporal dependence while retaining area-specific temporal trajectories, providing an alternative representation of spatio-temporal dependence. Inference is performed using Markov chain Monte Carlo methods. The approach is illustrated using monthly malaria incidence data from three Mozambican provinces and evaluated using the Root Mean Squared Error, Continuous Ranked Probability Score, empirical coverage probability, and credible interval width. Compared with established spatio-temporal models, the proposed framework achieves competitive predictive accuracy and better-calibrated predictive uncertainty. Spatially structuring the temporal hyperparameters also improves predictive interval calibration relative to an equivalent model with independent temporal processes, demonstrating the value of borrowing spatial information on temporal dependence and supporting this formulation as a competitive alternative to conventional outcome-level spatial smoothing for spatio-temporal areal data in disease surveillance.

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arXiv2026

Laplacian-P-splines for shared Gamma frailty models applied to clustered right-censored time-to-event data

Shared frailty models have been proposed to accommodate unmeasured cluster-specific risk factors through the inclusion of a common latent frailty term. Among possible frailty distributions, the Gamma distribution is appealing due to its non-negativity, flexibility, and algebraic tractability leading to closed-form marginal survival or hazard function expressions. Under the Bayesian paradigm, the posterior distributions of model parameters are usually explored with computationally intensive procedures relying on Markov chain Monte Carlo sampling. As an alternative, Laplacian-P-splines (LPS) provide a flexible and sampling-free alternative by relying on Gaussian approximations of the posterior target distributions. In this model class, analytical formulas are obtained for the gradient and Hessian, yielding a computationally efficient inference scheme for estimation of model parameters with a natural way of quantifying uncertainty. This article extends the LPS toolbox to the inclusion of shared Gamma frailty models for clustered time-to-event data. We assess the finite-sample performance of the LPS estimation procedure through an extensive simulation study and compare estimates with those obtained using penalized partial likelihood estimation, without specification of the baseline hazard, and with the variance of the frailty term being estimated using profile likelihood. Finally, the proposed LPS estimation method is exemplified using three publicly available biomedical datasets on: (i) recurrent infections in children, (ii) cancer prevention, and (iii) kidney transplantation.

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arXiv2025

Gibbs sampling for Bayesian P-splines

P-splines provide a flexible setting for modeling nonlinear model components based on a discretized penalty structure with a relatively simple computational backbone. Under a Bayesian inferential framework based on Markov chain Monte Carlo, estimates of model coefficients in P-splines models are typically obtained by means of Metropolis-type algorithms. These algorithms rely on a proposal distribution that has to be carefully chosen to generate Markov chains that efficiently explore the parameter space. To avoid such a sensitive tuning choice, we extend the Gibbs sampler to Bayesian P-splines models. In this model class, conditional posterior distributions of model coefficients are shown to have attractive mathematical properties. Taking advantage of these properties, we propose to sample the conditional posteriors by alternating between the adaptive rejection sampler when targets are log-concave and the Griddy-Gibbs sampler when targets are characterized by more complex shapes. The proposed Gibbs sampler for Bayesian P-splines (GSBPS) algorithm is shown to be an interesting tuning-free tool for inference in Bayesian P-splines models. Moreover, the GSBPS algorithm can be translated in a compact and user-friendly routine. After describing theoretical results, we illustrate the potential of our methodology in density estimation, Binomial regression, and smoothing of epidemic curves.

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arXiv2025

Distributed lag non-linear models with Laplacian-P-splines for analysis of spatially structured time series

Distributed lag non-linear models (DLNM) have gained popularity for modeling nonlinear lagged relationships between exposures and outcomes. When applied to spatially referenced data, these models must account for spatial dependence, a challenge that has yet to be thoroughly explored within the penalized DLNM framework. This gap is mainly due to the complex model structure and high computational demands, particularly when dealing with large spatio-temporal datasets. To address this, we propose a novel Bayesian DLNM-Laplacian-P-splines (DLNM-LPS) approach that incorporates spatial dependence using conditional autoregressive (CAR) priors, a method commonly applied in disease mapping. Our approach offers a flexible framework for capturing nonlinear associations while accounting for spatial dependence. It uses the Laplace approximation to approximate the conditional posterior distribution of the regression parameters, eliminating the need for Markov chain Monte Carlo (MCMC) sampling, often used in Bayesian inference, thus improving computational efficiency. The methodology is evaluated through simulation studies and applied to analyze the relationship between temperature and mortality in London.

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arXiv2024

A Low-Rank Bayesian Approach for Geoadditive Modeling

Kriging is an established methodology for predicting spatial data in geostatistics. Current kriging techniques can handle linear dependencies on spatially referenced covariates. Although splines have shown promise in capturing nonlinear dependencies of covariates, their combination with kriging, especially in handling count data, remains underexplored. This paper proposes a novel Bayesian approach to the low-rank representation of geoadditive models, which integrates splines and kriging to account for both spatial correlations and nonlinear dependencies of covariates. The proposed method accommodates Gaussian and count data inherent in many geospatial datasets. Additionally, Laplace approximations to selected posterior distributions enhances computational efficiency, resulting in faster computation times compared to Markov chain Monte Carlo techniques commonly used for Bayesian inference. Method performance is assessed through a simulation study, demonstrating the effectiveness of the proposed approach. The methodology is applied to the analysis of heavy metal concentrations in the Meuse river and vulnerability to the coronavirus disease 2019 (COVID-19) in Belgium. Through this work, we provide a new flexible and computationally efficient framework for analyzing spatial data.

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arXiv2022

Penalty parameter selection and asymmetry corrections to Laplace approximations in Bayesian P-splines models

Laplacian-P-splines (LPS) associate the P-splines smoother and the Laplace approximation in a unifying framework for fast and flexible inference under the Bayesian paradigm. Gaussian Markov field priors imposed on penalized latent variables and the Bernstein-von Mises theorem typically ensure a razor-sharp accuracy of the Laplace approximation to the posterior distribution of these variables. This accuracy can be seriously compromised for some unpenalized parameters, especially when the information synthesized by the prior and the likelihood is sparse. We propose a refined version of the LPS methodology by splitting the latent space in two subsets. The first set involves latent variables for which the joint posterior distribution is approached from a non-Gaussian perspective with an approximation scheme that is particularly well tailored to capture asymmetric patterns, while the posterior distribution for parameters in the complementary latent set undergoes a traditional treatment with Laplace approximations. As such, the dichotomization of the latent space provides the necessary structure for a separate treatment of model parameters, yielding improved estimation accuracy as compared to a setting where posterior quantities are uniformly handled with Laplace. In addition, the proposed enriched version of LPS remains entirely sampling-free, so that it operates at a computing speed that is far from reach to any existing Markov chain Monte Carlo approach. The methodology is illustrated on the additive proportional odds model with an application on ordinal survey data.

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arXiv2021

Laplacian-P-splines for Bayesian inference in the mixture cure model

The mixture cure model for analyzing survival data is characterized by the assumption that the population under study is divided into a group of subjects who will experience the event of interest over some finite time horizon and another group of cured subjects who will never experience the event irrespective of the duration of follow-up. When using the Bayesian paradigm for inference in survival models with a cure fraction, it is common practice to rely on Markov chain Monte Carlo (MCMC) methods to sample from posterior distributions. Although computationally feasible, the iterative nature of MCMC often implies long sampling times to explore the target space with chains that may suffer from slow convergence and poor mixing. An alternative strategy for fast and flexible sampling-free Bayesian inference in the mixture cure model is suggested in this paper by combining Laplace approximations and penalized B-splines. A logistic regression model is assumed for the cure proportion and a Cox proportional hazards model with a P-spline approximated baseline hazard is used to specify the conditional survival function of susceptible subjects. Laplace approximations to the conditional latent vector are based on analytical formulas for the gradient and Hessian of the log-likelihood, resulting in a substantial speed-up in approximating posterior distributions. The statistical performance and computational efficiency of the proposed Laplacian-P-splines mixture cure (LPSMC) model is assessed in a simulation study. Results show that LPSMC is an appealing alternative to classic MCMC for approximate Bayesian inference in standard mixture cure models. Finally, the novel LPSMC approach is illustrated on three applications involving real survival data.

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arXiv2020

Laplace approximation for fast Bayesian inference in generalized additive models based on penalized regression splines

Generalized additive models (GAMs) are a well-established statistical tool for modeling complex nonlinear relationships between covariates and a response assumed to have a conditional distribution in the exponential family. In this article, P-splines and the Laplace approximation are coupled for flexible and fast approximate Bayesian inference in GAMs. The proposed Laplace-P-spline model contributes to the development of a new methodology to explore the posterior penalty space by considering a deterministic grid-based strategy or a Markov chain sampler, depending on the number of smooth additive terms in the predictor. Our approach has the merit of relying on closed form analytical expressions for the gradient and Hessian of the approximate posterior penalty vector, which enables to construct accurate posterior pointwise and credible set estimators for latent field variables at a relatively low computational budget even for a large number of smooth additive components. Based upon simple Gaussian approximations of the conditional latent field posterior, the suggested methodology enjoys excellent statistical properties. The performance of the Laplace-P-spline model is confirmed through different simulation scenarios and the method is illustrated on two real datasets.

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