directional-statistics

Non-stationary wrapped Gaussian spatial response model

Isa Marques, Thomas Kneib and Nadja Klein / 2023-11-22

Circular data, i.e., data defined on the unit circle, can be found in many areas of science. The unique nature of these data means that conventional methods for non-circular data are not valid for these. At the same time, advances in geographical information and global positioning systems have…

bayesian-statistics

Linear-cost unbiased estimator for large crossed random effect models via couplings

Paolo Ceriani and Giacomo Zanella / 2023-09-27

In the following we show how it is possible to obtain parallelizable, unbiased and computationally cheap estimates of Crossed random effects models with a linear cost in the number of datapoints (and paramaters) exploiting couplings. […] CREM model a continuous response variables \(Y\) as…

bayesian-nonparametrics

Bayesian nonparametric modeling of conditional multidimensional dependence structures

Rosario Barone and Luciana Dalla Valle / 2023-05-19

In many real data applications we are often required to model jointly \(d\geq 3\) continuous random variables, denoted as \(Y_1,\dots,Y_d\) . The multivariate distribution, which allows us to describe the joint behaviour of those variables, can be denoted as \(F(Y_1,\dots,Y_d)=P(Y_1\le…

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…