%0 Journal Article %1 Pregibon1982 %A Pregibon, D %D 1982 %J Biometrics %K #EPIDEMIOLOGICMETHODS;#EPIDEMIOLOGY;HUMAN;MATH BIOLOGICAL;PROBABILITY;SUPPORT NON-P.H.S U.S.GOV'T %N 2 %P 485-498 %T Resistant fits for some commonly used logistic models with medical application %V 38 %X Logistic regression-type models are used in many applications. Some examples include the classical dose-response experiment, prospective and retrospective studies of disease incidence (with and without matching), and the analysis of ordinal data. In most instances, the model is fitted by the method of maximum likelihood, which, like least squares, is sensitive to atypical observations. An alternative to maximum likelihood is proposed and illustrated by examples.

@article{Pregibon1982, abstract = {Logistic regression-type models are used in many applications. Some examples include the classical dose-response experiment, prospective and retrospective studies of disease incidence (with and without matching), and the analysis of ordinal data. In most instances, the model is fitted by the method of maximum likelihood, which, like least squares, is sensitive to atypical observations. An alternative to maximum likelihood is proposed and illustrated by examples.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Pregibon, D}, biburl = {https://www.bibsonomy.org/bibtex/2cf17cfdc10b9998b5608f84a0965fea8/jepcastel}, interhash = {91d577f10d245f8c31eb7ed403de9325}, intrahash = {cf17cfdc10b9998b5608f84a0965fea8}, journal = {Biometrics}, keywords = {#EPIDEMIOLOGICMETHODS;#EPIDEMIOLOGY;HUMAN;MATH BIOLOGICAL;PROBABILITY;SUPPORT NON-P.H.S U.S.GOV'T}, note = {3262<m:linebreak></m:linebreak>Regressió logística}, number = 2, pages = {485-498}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Resistant fits for some commonly used logistic models with medical application}, volume = 38, year = 1982 }