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Robust Multinomial Logistic Regression

Robust multinomial logistic regression extends the standard multinomial logit model to handle outliers, influential observations, and mild misspecification of the response distribution. It replaces the conventional maximum likelihood score equations with bounded influence functions (M-estimation) or pairs maximum likelihood with sandwich variance estimators, so that a small fraction of anomalous cases cannot distort the estimated log-odds ratios across outcome categories.

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Taksonoomiline meetodikirje · regression-model / statistics

Cantoni, E., & Ronchetti, E. (2001). Robust inference for generalized linear models. Journal of the American Statistical Association, 96(455), 1022–1030. · DOI 10.1198/016214501753209004
Agresti, A. (2002). Categorical Data Analysis (2nd ed.). Wiley-Interscience. · ISBN 978-0471360933

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Taxonomic bucketGeneralized Linear Modelmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketMultinomial Logistic Regressionmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketOrdinal Logistic Regressionmachine-suggested · Relational suggestion, not evidence.Same method familyRobust Logistic Regressionmachine-suggested · Relational suggestion, not evidence.Taxonomic bucketRobust Regressionmachine-suggested · Relational suggestion, not evidence.