{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T16:48:29Z","timestamp":1785343709136,"version":"3.55.0"},"reference-count":83,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T00:00:00Z","timestamp":1617580800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Few problems in statistics are as perplexing as variable selection in the presence of very many redundant covariates. The variable selection problem is most familiar in parametric environments such as the linear model or additive variants thereof. In this work, we abandon the linear model framework, which can be quite detrimental when the covariates impact the outcome in a non-linear way, and turn to tree-based methods for variable selection. Such variable screening is traditionally done by pruning down large trees or by ranking variables based on some importance measure. Despite heavily used in practice, these ad hoc selection rules are not yet well understood from a theoretical point of view. In this work, we devise a Bayesian tree-based probabilistic method and show that it is consistent for variable selection when the regression surface is a smooth mix of p &amp;gt; n covariates. These results are the first model selection consistency results for Bayesian forest priors. Probabilistic assessment of variable importance is made feasible by a spike-and-slab wrapper around sum-of-trees priors. Sampling from posterior distributions over trees is inherently very difficult. As an alternative to Markov Chain Monte Carlo (MCMC), we propose approximate Bayesian computation (ABC) Bayesian forests, a new ABC sampling method based on data-splitting that achieves higher ABC acceptance rate. We show that the method is robust and successful at finding variables with high marginal inclusion probabilities. Our ABC algorithm provides a new avenue towards approximating the median probability model in non-parametric setups where the marginal likelihood is intractable.<\/jats:p>","DOI":"10.1111\/rssb.12423","type":"journal-article","created":{"date-parts":[[2021,4,5]],"date-time":"2021-04-05T08:58:53Z","timestamp":1617613133000},"page":"453-481","source":"Crossref","is-referenced-by-count":8,"title":["Variable Selection with ABC Bayesian Forests"],"prefix":"10.1093","volume":"83","author":[{"given":"Yi","family":"Liu","sequence":"first","affiliation":[{"name":"Department of Statistics, University of Chicago , Chicago , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Veronika","family":"Ro\u010dkov\u00e1","sequence":"additional","affiliation":[{"name":"Booth School of Business, University of Chicago , Chicago , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuexi","family":"Wang","sequence":"additional","affiliation":[{"name":"Booth School of Business, University of Chicago , Chicago , USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,4,5]]},"reference":[{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"2055","DOI":"10.1214\/15-AOS1337","article-title":"Controlling the false discovery rate via knockoffs","volume":"43","author":"Barber","year":"2015","journal-title":"The Annals of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1214\/15-EJS988","article-title":"The rate of convergence for approximate Bayesian computation","volume":"9","author":"Barber","year":"2015","journal-title":"Electronic Journal of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"870","DOI":"10.1214\/009053604000000238","article-title":"Optimal predictive model selection","volume":"32","author":"Barbieri","year":"2004","journal-title":"The Annals of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1093\/oso\/9780198523567.003.0002","article-title":"The intrinsic Bayes factor for linear models","volume":"5","author":"Berger","year":"1996","journal-title":"Bayesian Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1214\/009053604000000229","article-title":"Training samples in objective Bayesian model selection","volume":"32","author":"Berger","year":"2004","journal-title":"The Annals of Statistics"},{"key":"2023022408371614500_","first-page":"1750","article-title":"Variable selection for BART: An application to gene regulation","author":"Bleich","year":"2014","journal-title":"The Annals of Applied Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Machine Learning"},{"key":"2023022408371614500_","volume-title":"Online manual for random forests","author":"Breiman","year":"2013"},{"key":"2023022408371614500_","volume-title":"Classification and regression trees","author":"Breiman","year":"1984"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"551","DOI":"10.1111\/rssb.12265","article-title":"Panning for gold: \u2018model-x\u2019 knockoffs for high dimensional controlled variable selection","volume":"80","author":"Candes","year":"2018","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1214\/12-BA703","article-title":"Scalable variational inference for bayesian variable selection in regression, and its accuracy in genetic association studies","volume":"7","author":"Carbonetto","year":"2012","journal-title":"Bayesian Analysis"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","DOI":"10.1214\/18-EJS1494","volume-title":"Empirical Bayes analysis of spike and slab posterior distributions","author":"Castillo","year":"2018"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"2069","DOI":"10.1214\/12-AOS1029","article-title":"Needles and straw in a haystack: Posterior concentration for possibly sparse sequences","volume":"40","author":"Castillo","year":"2012","journal-title":"The Annals of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"1986","DOI":"10.1214\/15-AOS1334","article-title":"Bayesian linear regression with sparse priors","volume":"43","author":"Castillo","year":"2015","journal-title":"The Annals of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1080\/01621459.1998.10473750","article-title":"Bayesian CART model search","volume":"93","author":"Chipman","year":"1998","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408371614500_","first-page":"65","volume-title":"The practical implementation of Bayesian model selection","author":"Chipman","year":"2001"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1214\/09-AOAS285","article-title":"BART: Bayesian additive regression trees","volume":"4","author":"Chipman","year":"2010","journal-title":"The Annals of Applied Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"2667","DOI":"10.1214\/12-AOS1046","article-title":"Tight conditions for consistency of variable selection in the context of high dimensionality","volume":"40","author":"Comminges","year":"2012","journal-title":"The Annals of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"410","DOI":"10.1016\/j.tree.2010.04.001","article-title":"Approximate Bayesian computation (ABC) in practice","volume":"25","author":"Csillery","year":"2010","journal-title":"Trends in Ecology & Evolution"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1093\/biomet\/85.2.363","article-title":"A Bayesian CART algorithm","volume":"85","author":"Denison","year":"1998","journal-title":"Biometrika"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1214\/12-AOAS571","article-title":"Bayesian inference and the parametric bootstrap","volume":"6","author":"Efron","year":"2012","journal-title":"The Annals of Applied Statistics"},{"issue":"5","key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1214\/aos\/1024691353","article-title":"The problem of regions","volume":"26","author":"Efron","year":"1998","journal-title":"The Annals of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"1348","DOI":"10.1198\/016214501753382273","article-title":"Variable selection via nonconcave penalized likelihood and its oracle properties","volume":"96","author":"Fan","year":"2001","journal-title":"Journal of the American statistical Association"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"783","DOI":"10.2307\/2408678","article-title":"Confidence limits on phylogenies: An approach using the bootstrap","volume":"39","author":"Felsenstein","year":"1985","journal-title":"Evolution"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1093\/biomet\/asy027","article-title":"Asymptotic properties of approximate Bayesian computation","volume":"105","author":"Frazier","year":"2018","journal-title":"Biometrika"},{"issue":"2","key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"421","DOI":"10.1111\/rssb.12356","article-title":"Model misspecification in approximate Bayesian computation: Consequences and diagnostics","volume":"82","author":"Frazier","year":"2020","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"},{"key":"2023022408371614500_","first-page":"1","article-title":"Multivariate adaptive regression splines","volume":"19","author":"Friedman","year":"1991","journal-title":"The Annals of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v033.i01","article-title":"Regularization paths for generalized linear models via coordinate descent","volume":"33","author":"Friedman","year":"2010","journal-title":"Journal of Statistical Software"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1080\/01621459.1993.10476353","article-title":"Variable selection via Gibbs sampling","volume":"88","author":"George","year":"1993","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1214\/07-EJS090","article-title":"Nonparametric Bayesian model selection and averaging","volume":"2","author":"Ghosal","year":"2008","journal-title":"Electronic Journal of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/S0378-3758(01)00222-1","article-title":"Nonsubjective Bayes testing? An overview","volume":"103","author":"Ghosh","year":"2002","journal-title":"Journal of Statistical Planning and Inference"},{"key":"2023022408371614500_","volume-title":"Probability and the weighing of evidence","author":"Good","year":"1950"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"1119","DOI":"10.1198\/016214508000000689","article-title":"Bayesian treed Gaussian process models with an application to computer modeling","volume":"103","author":"Gramacy","year":"2008","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.crma.2008.12.009","article-title":"ABC methods for model choice in Gibbs random fields","volume":"347","author":"Grelaud","year":"2009","journal-title":"Comptes Rendus Mathematique"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"1780","DOI":"10.1214\/11-AOAS455","article-title":"Bayesian variable selection regression for genome-wide association studies and other large-scale problems","volume":"5","author":"Guan","year":"2011","journal-title":"The Annals of Applied Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1198\/jcgs.2010.08162","article-title":"Bayesian nonparametric modeling for causal inference","volume":"20","author":"Hill","year":"2011","journal-title":"Journal of Computational and Graphical Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1214\/07-EJS039","article-title":"Variable importance in binary regression trees and forests","volume":"1","author":"Ishwaran","year":"2007","journal-title":"Electronic Journal of Statistics"},{"key":"2023022408371614500_","first-page":"1595","article-title":"Learning summary statistic for approximate Bayesian computation via deep neural network","volume":"27","author":"Jiang","year":"2017","journal-title":"Statistica Sinica"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1080\/01621459.2012.682536","article-title":"Bayesian model selection in high-dimensional settings","volume":"107","author":"Johnson","year":"2012","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408371614500_","first-page":"425","volume-title":"Variable importance using decision trees","author":"Kazemitabar","year":"2017"},{"key":"2023022408371614500_","first-page":"293","volume-title":"Iterative Markov Chain Monte Carlo computation of reference priors and minimax risk","author":"Lafferty","year":"2001"},{"issue":"1","key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1214\/009053607000000811","article-title":"RODEO: Sparse, greedy nonparametric regression","volume":"36","author":"Lafferty","year":"2008","journal-title":"The Annals of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1524\/stnd.2007.25.2.127","article-title":"On universal Bayesian adaptation","volume":"25","author":"Lember","year":"2007","journal-title":"Statistics & Decisions"},{"key":"2023022408371614500_","volume-title":"Posterior probabilities of alternative linear models","author":"Lempers","year":"1971"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1093\/biomet\/asx081","article-title":"Convergence of regression-adjusted approximate Bayesian computation","volume":"105","author":"Li","year":"2018","journal-title":"Biometrika"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"955","DOI":"10.1080\/01621459.2017.1409122","article-title":"Bayesian neural networks for selection of drug sensitive genes","volume":"113","author":"Liang","year":"2018","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408371614500_","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"2272","DOI":"10.1214\/009053606000000722","article-title":"Component selection and smoothing in multivariate nonparametric regression","volume":"34","author":"Lin","year":"2006","journal-title":"The Annals of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/01621459.2016.1264957","article-title":"Bayesian regression trees for high-dimensional prediction and variable selection","volume":"113","author":"Linero","year":"2018","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"1167","DOI":"10.1007\/s11222-011-9288-2","article-title":"Approximate Bayesian computational methods","volume":"22","author":"Marin","year":"2012","journal-title":"Statistics and Computing"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1080\/07362994.2013.879262","article-title":"Approximate Bayesian computation for smoothing","volume":"32","author":"Martin","year":"2014","journal-title":"Stochastic Analysis and Applications"},{"key":"2023022408371614500_","volume-title":"BART: Bayesian Additive Regression Trees","author":"McCulloch","year":"2018"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"228","DOI":"10.1214\/14-STS508","article-title":"Posterior model consistency in variable selection as the model dimension grows","volume":"30","author":"Moreno","year":"2015","journal-title":"Statistical Science"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1214\/14-AOS1207","article-title":"Bayesian variable selection with shrinking and diffusing priors","volume":"42","author":"Narisetty","year":"2014","journal-title":"The Annals of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1111\/j.2517-6161.1994.tb01956.x","article-title":"Approximate Bayesian inference with the weighted likelihood bootstrap","volume":"56","author":"Newton","year":"1994","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1111\/j.2517-6161.1995.tb02017.x","article-title":"Fractional bayes factors for model comparison","volume":"57","author":"O\u2019Hagan","year":"1995","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1093\/biomet\/89.3.491","article-title":"Expected-posterior prior distributions for model selection","volume":"89","author":"P\u00e9rez","year":"2002","journal-title":"Biometrika"},{"key":"2023022408371614500_","first-page":"99","volume-title":"Approximate Bayesian Computation and MCMC","author":"Plagnol","year":"2004"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1093\/bioinformatics\/btv684","article-title":"Reliable ABC model choice via random forests","volume":"32","author":"Pudlo","year":"2015","journal-title":"Bioinformatics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"1541","DOI":"10.1198\/jasa.2010.tm10130","article-title":"Variable selection using adaptive nonlinear interaction structures in high dimensions","volume":"105","author":"Radchenko","year":"2010","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1111\/j.1467-9868.2009.00718.x","article-title":"Sparse additive models","volume":"71","author":"Ravikumar","year":"2009","journal-title":"Journal of the Royal Statistical Society: Series B (Statistical Methodology)"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1086\/431601","article-title":"Hiv-1 protease and reverse-transcriptase mutations: correlations with antiretroviral therapy in subtype b isolates and implications for drug-resistance surveillance","volume":"192","author":"Rhee","year":"2005","journal-title":"The Journal of infectious diseases"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"17355","DOI":"10.1073\/pnas.0607274103","article-title":"Genotypic predictors of human immunodeficiency virus type 1 drug resistance","volume":"103","author":"Rhee","year":"2006","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"15112","DOI":"10.1073\/pnas.1102900108","article-title":"Lack of confidence in approximate Bayesian computation model choice","volume":"108","author":"Robert","year":"2011","journal-title":"Proceedings of the National Academy of Sciences"},{"key":"2023022408371614500_","first-page":"1","article-title":"Particle EM for variable selection","volume":"113","author":"Ro\u010dkov\u00e1","year":"2017","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1080\/01621459.2013.869223","article-title":"EMVS: The EM approach to Bayesian variable selection","volume":"109","author":"Ro\u010dkov\u00e1","year":"2014","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1080\/01621459.2016.1260469","article-title":"The Spike-and-Slab LASSO","volume":"113","author":"Ro\u010dkov\u00e1","year":"2018","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408371614500_","first-page":"2108","article-title":"Posterior concentration for Bayesian regression trees and their ensembles","volume":"48","author":"Ro\u010dkov\u00e1","year":"2020","journal-title":"The Annals of Statistics (in revision)"},{"key":"2023022408371614500_","volume-title":"On theory for BART","author":"Ro\u010dkov\u00e1","year":"2019"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"130","DOI":"10.1214\/11-STS354","article-title":"Variable selection for nonparametric Gaussian process priors: Models and computational strategies","volume":"26","author":"Savitsky","year":"2011","journal-title":"Statistical Science"},{"key":"2023022408371614500_","volume-title":"spikeslabgam: Bayesian variable selection, model choice and regularization for generalized additive mixed models in r","author":"Scheipl","year":"2011"},{"key":"2023022408371614500_","first-page":"2587","article-title":"Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem","author":"Scott","year":"2010","journal-title":"The Annals of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"e1002803","DOI":"10.1371\/journal.pcbi.1002803","article-title":"Approximate Bayesian computation","volume":"9","author":"Sunnaaker","year":"2013","journal-title":"PLoS computational biology"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1198\/jasa.2011.ap09769","article-title":"Dynamic trees for learning and design","volume":"106","author":"Taddy","year":"2011","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1198\/jasa.2011.ap09769","article-title":"Dynamic trees for learning and design","volume":"106","author":"Taddy","year":"2011","journal-title":"Journal of the American Statistical Association"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1093\/genetics\/145.2.505","article-title":"Inferring coalescence times from DNA sequence data","volume":"145","author":"Tavar\u00e9","year":"1997","journal-title":"Genetics"},{"key":"2023022408371614500_","volume-title":"rpart: Recursive Partitioning and Regression Trees","author":"Therneau","year":"2018"},{"issue":"1","key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1111\/j.2517-6161.1996.tb02080.x","article-title":"Regression shrinkage and selection via the Lasso","volume":"58","author":"Tibshirani","year":"1996","journal-title":"Journal of the Royal Statistical Society: Series B (Methodological)"},{"key":"2023022408371614500_","first-page":"481","article-title":"Discussion on least angle regression","volume":"32","author":"Turlach","year":"2004","journal-title":"The Annals of Statistics"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"1360","DOI":"10.1214\/09-EJS506","article-title":"On the conditions used to prove oracle results for the lasso","volume":"3","author":"Van De Geer","year":"2009","journal-title":"Electronic Journal of Statistics"},{"key":"2023022408371614500_","volume-title":"Bayesian model selection consistency and oracle inequality with intractable marginal likelihood","author":"Yang","year":"2017"},{"key":"2023022408371614500_","first-page":"2541","article-title":"On model selection consistency of LASSO","volume":"7","author":"Zhao","year":"2006","journal-title":"Journal of Machine Learning Research"},{"key":"2023022408371614500_","doi-asserted-by":"crossref","first-page":"1770","DOI":"10.1080\/01621459.2015.1036994","article-title":"Reinforcement learning trees","volume":"110","author":"Zhu","year":"2015","journal-title":"Journal of the American Statistical Association"}],"container-title":["Journal of the Royal Statistical Society Series B: Statistical Methodology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/rssb.12423","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1111\/rssb.12423","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/academic.oup.com\/jrsssb\/article-pdf\/83\/3\/453\/49321152\/jrsssb_83_3_453.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jrsssb\/article-pdf\/83\/3\/453\/49321152\/jrsssb_83_3_453.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T15:09:52Z","timestamp":1724771392000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jrsssb\/article\/83\/3\/453\/7056062"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,5]]},"references-count":83,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,4,5]]},"published-print":{"date-parts":[[2021,7,1]]}},"URL":"https:\/\/doi.org\/10.1111\/rssb.12423","relation":{},"ISSN":["1369-7412","1467-9868"],"issn-type":[{"value":"1369-7412","type":"print"},{"value":"1467-9868","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,7]]},"published":{"date-parts":[[2021,4,5]]}}}