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Interpreting multinomial logistic regression [PDF]

Stata Technical Bulletin, 1994
Social and biological scientists widely use logit (logistic) regression to model binary dependent variables such as move/stay or live/die. Techniques for modeling multiple-category dependent variables are a relatively recent development, however. Asking Stata to perform multinomial logistic regression is easy; given a Y with three or more unordered ...
Hamilton, Lawrence C., Seyfrit, Carole L.   +1 more
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Multinomial logistic regression algorithm

Annals of the Institute of Statistical Mathematics, 1992
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Multinomial Logistic Regression Analysis

2017
The usage of mixed methods approach on qualitative data has been exemplified in this chapter. The chapter presents the relevance of using multinomial regression approach in the study and discusses its results. The chapter is insightful for readers looking forward to learning practical applications of quantitative techniques on qualitative data.
Nausheen Nizami, Narayan Prasad
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Multinomial Squared Direction Cosines Regression

The 2011 International Joint Conference on Neural Networks, 2011
In this paper we introduce Multinomial Squared Direction Cosines Regression as an alternative Multinomial Response Model. The proposed model offers an intuitive geometric interpretation to the task of estimating posterior class probabilities in multi-class problems.
Naveed H. Iqbal, Georgios C. Anagnostopoulos   +1 more
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Multinomial Latent Logistic Regression for Image Understanding

IEEE Transactions on Image Processing, 2016
In this paper, we present multinomial latent logistic regression (MLLR), a new learning paradigm that introduces latent variables to logistic regression. By inheriting the advantages of logistic regression, MLLR is efficiently optimized using the second-order derivatives and provides effective probabilistic analysis on output predictions.
Zhe, Xu, Zhibin, Hong, Ya, Zhang, Junjie, Wu, Ah Chung, Tsoi, Dacheng, Tao   +5 more
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Multinomial structuring in linear regression

Model Assisted Statistics and Applications, 2008
Ordinary linear regression produces a good fit for the observations close to the mean point. To improve the fit for the values far from the mean point, an implement by the multinomial logit model is suggested. Segmenting the values of the dependent variable to several sections, it is possible to present a theoretical model via a linear aggregate of the
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Multinomial Logistic Regression

2023
Kamarul Imran Musa, Wan Nor Arifin Wan Mansor, Tengku Muhammad Hanis   +2 more
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Multinomial Logistic Regression (55 Patients)

2012
Primary question: the numbers of patients falling out of bed with and without injury are assessed in two hospital departments. It is expected that the department of internal medicine will have higher scores.
Ton J. Cleophas, Aeilko H. Zwinderman
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Multinomial regression models based on continuation ratios

Statistics in Medicine, 1988
AbstractThis paper concerns continuation ratio models for multinomial responses. These are conditional probabilities used in logit models to define the dependence of the multinomial proportions on explanatory variables and unknown parameters. A distinctive feature of these models is that if one models the various continuation ratios separately, then ...
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Multinomial Principal Component Logistic Regression on Shape Data

Journal of Classification, 2022
Meisam Moghimbeygi, A. Nodehi
semanticscholar   +1 more source