robust-statistics

Fitting robust non-Gaussian models in Stan and R-INLA

Rafael Cabral, David Bolin and Håvard Rue / 2023-01-19

Traditionally the excitation noise of spatial and temporal models is Gaussian. Take, for instance, an AR1 (autoregressive of order 1) process, where the increments \(x_{i+1}-\rho x_i, \ \ |\rho|<1\) are assumed to follow a Gaussian distribution. However, it is easy to find datasets that contain…

webinars

Selection of Priors in Bayesian Structural Equation Modeling

2022-02-14

Selection of Priors in Bayesian Structural Equation Modelling Structural equation modeling (SEM) is an important framework within the social sciences that encompasses a wide variety of statistical models. Traditionally, estimation of SEMs has relied on maximum likelihood. Unfortunately, there also…

webinars

Recent Advances in Approximate Bayesian Inference

2022-02-08

Recent Advances in Approximate Bayesian Inference In approximate Bayesian computation, likelihood function is intractable and needs to be itself estimated using forward simulations of the statistical model (Beaumont et al., 2002; Marin et al., 2012; Sisson et al., 2019; Martin et al., 2020). Recent…

bayesian-statistics

Measuring dependence in the Wasserstein distance for Bayesian nonparametric models

Marta Catalano, Antonio Lijoi and Igor Prünster / 2022-01-17

Bayesian nonparametric (BNP) models are a prominent tool for performing flexible inference with a natural quantification of uncertainty. Traditionallly, flexible inference within a homogeneous sample is performed with exchangeable models of the type \(X_1,\dots, X_n|\tilde \mu \sim T(\tilde \mu)\),…

robust-statistics

Universal estimation with Maximum Mean Discrepancy (MMD)

Pierre Alquier / 2022-01-13

This is an updated version of a blog post on RIKEN AIP Approximate Bayesian Inference team webpage: https://team-approx-bayes.github.io/blog/mmd/ […] A very old and yet very exciting problem in statistics is the definition of a universal estimator \(\hat{\theta}\). An estimation procedure…

nonparametric-statistics

Optimal disclosure risk assessment

Federico Camerlenghi, Stefano Favaro, Zacharie Naulet and Francesca Panero / 2021-09-30

Protection against disclosure is a legal and ethical obligation for agencies releasing microdata files for public use. Consider a microdata sample \({X}_n=(X_{1},\ldots,X_{n})\) of size \(n\) from a finite population of size \(\bar{n}=n+\lambda n\), with \(\lambda>0\), such that each sample…