{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T10:34:46Z","timestamp":1788950086293,"version":"build-2803163510"},"reference-count":46,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2025,10,21]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This article focuses on inference in logistic regression for high-dimensional binary outcomes. A popular approach induces dependence across the outcomes by including latent factors in the linear predictor. Bayesian approaches are useful for characterizing uncertainty in inferring the regression coefficients, factors and loadings, while also incorporating hierarchical and shrinkage structures. However, Markov chain Monte Carlo algorithms for posterior computation face challenges in scaling to high-dimensional outcomes. Motivated by applications in ecology, we exploit a blessing of dimensionality to motivate pre-estimation of the latent factors. Conditionally on the factors, the outcomes are modelled via independent logistic regressions. We implement Gaussian approximations in parallel in inferring the posterior on the regression coefficients and loadings, including a simple adjustment to obtain credible intervals with valid frequentist coverage. We show posterior concentration properties and excellent empirical performance in simulations. The methods are applied to arthropod biodiversity data in Madagascar.<\/jats:p>","DOI":"10.1093\/biomet\/asaf056","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T10:11:55Z","timestamp":1752747115000},"source":"Crossref","is-referenced-by-count":4,"title":["Factor pretraining in Bayesian multivariate logistic models"],"prefix":"10.1093","volume":"112","author":[{"given":"L","family":"Mauri","sequence":"first","affiliation":[{"name":"Department of Statistical Science, Duke University , Box 90251 , Durham, North Carolina 27708,","place":["U.S.A"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"D B","family":"Dunson","sequence":"additional","affiliation":[{"name":"Department of Statistical Science, Duke University , Box 90251 , Durham, North Carolina 27708,","place":["U.S.A"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2025,7,21]]},"reference":[{"key":"2026032607100571600_asaf056-B2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41586-024-07658-9","article-title":"Airborne DNA reveals predictable spatial and seasonal dynamics of fungi","volume":"631","author":"Abrego","year":"2024","journal-title":"Nature"},{"key":"2026032607100571600_asaf056-B1","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1111\/1755-0998.12755","article-title":"Give me a sample of air and I will tell which species are found from your region: molecular identification of fungi from airborne spore samples","volume":"18","author":"Abrego","year":"2018","journal-title":"Molec. 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