{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T11:46:58Z","timestamp":1772106418775,"version":"3.50.1"},"reference-count":27,"publisher":"World Scientific Pub Co Pte Lt","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Adv. Adapt. Data Anal."],"published-print":{"date-parts":[[2011,7]]},"abstract":"<jats:p> For a categorical variable with several outcomes, its dependence on the predictors is usually considered in the conditional or multinomial logit models. This work considers elasticity features of the binary and categorical logits and introduces the coefficients individual by observations. The paper shows that by a special rearrangement of data the more complicated conditional and multinomial models can be reduced to binary logistic regression. It suggests the usage of any software widely available for logit modeling to facilitate constructing for complex conditional and multinomial regressions. In addition, for binary logit, it is possible to obtain meaningful coefficients of regression by transforming data to the linear link function, which opens a possibility to obtain meaningful parameters of the complicated models with categorical dependent variables. <\/jats:p>","DOI":"10.1142\/s1793536911000738","type":"journal-article","created":{"date-parts":[[2011,10,24]],"date-time":"2011-10-24T05:37:59Z","timestamp":1319434679000},"page":"309-324","source":"Crossref","is-referenced-by-count":9,"title":["CONDITIONAL AND MULTINOMIAL LOGITS AS BINARY LOGIT REGRESSIONS"],"prefix":"10.1142","volume":"03","author":[{"given":"STAN","family":"LIPOVETSKY","sequence":"first","affiliation":[{"name":"GfK Custom Research North America, 8401 Golden Valley Road, Minneapolis, MN 55427, USA"}]}],"member":"219","published-online":{"date-parts":[[2012,4,5]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4899-1292-3"},{"key":"rf2","volume-title":"Discrete Choice Analysis","author":"Ben-Akiva M.","year":"1985"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1111\/j.1467-937X.2004.00298.x"},{"key":"rf4","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop C. 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