A New Family of BAN Estimators for Polytomous Logistic Regression Models based on ϕ- Divergence Measures
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DOI: 10.1007/s10260-006-0008-6
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References listed on IDEAS
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Cited by:
- D.M. Sakate & D.N. Kashid, 2014. "Variable selection via penalized minimum φ-divergence estimation in logistic regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1233-1246, June.
- Pardo, L. & Pardo, M.C., 2008. "An extension of likelihood-ratio-test for testing linear hypotheses in the baseline-category logit model," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1477-1489, January.
- Elena Castilla & Abhik Ghosh & Nirian Martin & Leandro Pardo, 2021. "Robust semiparametric inference for polytomous logistic regression with complex survey design," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(3), pages 701-734, September.
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Keywords
Multinomial sampling; Kullback–Leibler divergence measure; Minimum ϕ-divergence estimator; Polytomous regression model;All these keywords.
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