{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T08:19:37Z","timestamp":1790237977728,"version":"4.1.0"},"reference-count":45,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2019,10,26]],"date-time":"2019-10-26T00:00:00Z","timestamp":1572048000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"funder":[{"DOI":"10.13039\/501100008982","name":"National Science Foundation","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008982","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,12,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>We study a class of missingness mechanisms, referred to as sequentially additive nonignorable, for modelling multivariate data with item nonresponse. These mechanisms explicitly allow the probability of nonresponse for each variable to depend on the value of that variable, thereby representing nonignorable missingness mechanisms. These missing data models are identified by making use of auxiliary information on marginal distributions, such as marginal probabilities for multivariate categorical variables or moments for numeric variables. We prove identification results and illustrate the use of these mechanisms in an application.<\/jats:p>","DOI":"10.1093\/biomet\/asz054","type":"journal-article","created":{"date-parts":[[2019,9,4]],"date-time":"2019-09-04T15:27:55Z","timestamp":1567610875000},"page":"889-911","source":"Crossref","is-referenced-by-count":15,"title":["Sequentially additive nonignorable missing data modelling using auxiliary marginal information"],"prefix":"10.1093","volume":"106","author":[{"given":"Mauricio","family":"Sadinle","sequence":"first","affiliation":[{"name":"Department of Biostatistics, University of Washington, Seattle, Washington, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jerome P","family":"Reiter","sequence":"additional","affiliation":[{"name":"Department of Statistical Science, Duke University, 214 Old Chemistry Building, Durham, North Carolina, U.S.A"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,10,26]]},"reference":[{"key":"2019111117081012100_B1","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1080\/01621459.1993.10476321","article-title":"Bayesian analysis of binary and polychotomous response data","volume":"88","author":"Albert,","year":"1993","journal-title":"J. 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