Powering research through innovative methods for mixtures in epidemiology (PRIME) program: novel and expanded statistical methods
BR Joubert, MA Kioumourtzoglou… - International Journal of …, 2022 - mdpi.com
Humans are exposed to a diverse mixture of chemical and non-chemical exposures across
their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure …
their lifetimes. Well-designed epidemiology studies as well as sophisticated exposure …
Sparse Bayesian factor analysis when the number of factors is unknown
S Frühwirth-Schnatter, D Hosszejni… - Bayesian Analysis, 2024 - projecteuclid.org
There has been increased research interest in the subfield of sparse Bayesian factor
analysis with shrinkage priors, which achieve additional sparsity beyond the natural …
analysis with shrinkage priors, which achieve additional sparsity beyond the natural …
spAbundance: An R package for single‐species and multi‐species spatially explicit abundance models
Numerous modelling techniques exist to estimate abundance of plant and animal
populations. The most accurate methods account for multiple complexities found in …
populations. The most accurate methods account for multiple complexities found in …
Inferring covariance structure from multiple data sources via subspace factor analysis
Factor analysis provides a canonical framework for imposing lower-dimensional structure
such as sparse covariance in high-dimensional data. High-dimensional data on the same …
such as sparse covariance in high-dimensional data. High-dimensional data on the same …
Efficiently resolving rotational ambiguity in Bayesian matrix sampling with matching
E Poworoznek, N Anceschi, F Ferrari… - arXiv preprint arXiv …, 2021 - arxiv.org
A wide class of Bayesian models involve unidentifiable random matrices that display
rotational ambiguity, with the Gaussian factor model being a typical example. A rich variety …
rotational ambiguity, with the Gaussian factor model being a typical example. A rich variety …
Linear-cost unbiased posterior estimates for crossed effects and matrix factorization models via couplings
PM Ceriani, G Zanella - arXiv preprint arXiv:2410.08939, 2024 - arxiv.org
We design and analyze unbiased Markov chain Monte Carlo (MCMC) schemes based on
couplings of blocked Gibbs samplers (BGSs), whose total computational costs scale linearly …
couplings of blocked Gibbs samplers (BGSs), whose total computational costs scale linearly …
Fast variational inference for Bayesian factor analysis in single and multi-study settings
B Hansen, A Avalos-Pacheco, M Russo… - … of Computational and …, 2024 - Taylor & Francis
Factors models are commonly used to analyze high-dimensional data in both single-study
and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte …
and multi-study settings. Bayesian inference for such models relies on Markov Chain Monte …
[HTML][HTML] Creating area level indices of behaviours impacting cancer in Australia with a Bayesian generalised shared component model
This study develops a model-based index approach called the Generalised Shared
Component Model (GSCM) by drawing on the large field of factor models. The proposed …
Component Model (GSCM) by drawing on the large field of factor models. The proposed …
Adaptive partition Factor Analysis
E Bortolato, A Canale - arXiv preprint arXiv:2410.18939, 2024 - arxiv.org
Factor Analysis has traditionally been utilized across diverse disciplines to extrapolate latent
traits that influence the behavior of multivariate observed variables. Historically, the focus …
traits that influence the behavior of multivariate observed variables. Historically, the focus …
Dynamic factor analysis with dependent Gaussian processes for high-dimensional gene expression trajectories
The increasing availability of high-dimensional, longitudinal measures of gene expression
can facilitate understanding of biological mechanisms, as required for precision medicine …
can facilitate understanding of biological mechanisms, as required for precision medicine …