3 citations · 16 across the 12 of their papers we have counts for
11 papers · 1 filter
Demystifying Spatial Confounding
Emiko Dupont, Isa Marques, Thomas Kneib
Spatial confounding is a fundamental issue in spatial regression models which arises because spatial random effects, included to approximate unmeasured spatial variation, are typic…
Bayesian Discrete Conditional Transformation Models
Manuel Carlan, Thomas Kneib
We propose a novel Bayesian model framework for discrete ordinal and count data based on conditional transformations of the responses. The conditional transformation function is es…
A multivariate Gaussian random field prior against spatial confounding
Isa Marques, Thomas Kneib, Nadja Klein
Spatial models are used in a variety research areas, such as environmental sciences, epidemiology, or physics. A common phenomenon in many spatial regression models is spatial conf…
Flexible Bayesian Modeling of Counts: Constructing Penalized Complexity Priors
Mahsa Nadifar, Hossein Baghishani, Thomas Kneib +1
Many of the data, particularly in medicine and disease mapping are count. Indeed, the under or overdispersion problem in count data distrusts the performance of the classical Poiss…
Adaptive shrinkage of smooth functional effects towards a predefined functional subspace
Paul Wiemann, Thomas Kneib
In this paper, we propose a new horseshoe-type prior hierarchy for adaptively shrinking spline-based functional effects towards a predefined vector space of parametric functions. I…
Beyond unidimensional poverty analysis using distributional copula models for mixed ordered-continuous outcomes
Maike Hohberg, Francesco Donat, Giampiero Marra +1
Poverty is a multidimensional concept often comprising a monetary outcome and other welfare dimensions such as education, subjective well-being or health, that are measured on an o…