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Polytomous logistic regression

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  • J. Engel

Abstract

In this paper a review will be given of some methods available for modelling relationships between categorical response variables and explanatory variables. These methods are all classed under the name polytomous logistic regression (PLR). Models for PLR will be presented and compared; model parameters will be tested and estimated by weighted least squares and by likelihood. Usually, software is needed for computation, and available statistical software is reported. An industrial problem is solved to some extent as an example to illustrate the use of PLR. The paper is concluded by a discussion on the various PLR‐methods and some topics that need a further study are mentioned.

Suggested Citation

  • J. Engel, 1988. "Polytomous logistic regression," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 42(4), pages 233-252, December.
    • Handle: RePEc:bla:stanee:v:42:y:1988:i:4:p:233-252
    DOI: 10.1111/j.1467-9574.1988.tb01238.x
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    Cited by:

    1. Schumacher, Martin & Ro[ss]ner, Reinhard & Vach, Werner, 1996. "Neural networks and logistic regression: Part I," Computational Statistics & Data Analysis, Elsevier, vol. 21(6), pages 661-682, June.
    2. Thien Khai Tran & Hoa Dinh & Hien Nguyen & Dac-Nhuong Le & Dong-Ky Nguyen & An C. Tran & Viet Nguyen-Hoang & Ha Nguyen Thi Thu & Dinh Hung & Suong Tieu & Canh Khuu & Tuan A. Nguyen, 2021. "The Impact of the COVID-19 Pandemic on College Students: An Online Survey," Sustainability, MDPI, vol. 13(19), pages 1-19, September.
    3. Amy R. Mulick & Shefali Oza & David Prieto‐Merino & Francisco Villavicencio & Simon Cousens & Jamie Perin, 2022. "A Bayesian hierarchical model with integrated covariate selection and misclassification matrices to estimate neonatal and child causes of death," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2097-2120, October.
    4. Kuss, Oliver, 2006. "On the estimation of the stereotype regression model," Computational Statistics & Data Analysis, Elsevier, vol. 50(8), pages 1877-1890, April.
    5. A. Gupta & D. Kasturiratna & T. Nguyen & L. Pardo, 2006. "A New Family of BAN Estimators for Polytomous Logistic Regression Models based on ϕ- Divergence Measures," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(2), pages 159-176, August.
    6. Elena Castilla & Nirian Martín & Leandro Pardo, 2018. "Minimum phi-divergence estimators for multinomial logistic regression with complex sample design," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(3), pages 381-411, July.
    7. A. K. Gupta & D. Kasturiratna & T. Nguyen & L. Pardo, 2006. "A New Family of BAN Estimators for Polytomous Logistic Regression Models based on ϕ- Divergence Measures," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(2), pages 159-176, August.

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