Logistic and multinomial-logit models: A brief review on their modifications and extensions

Article type: Research Article

Authors: Lipovetsky, Stan

Affiliations: Minneapolis, Minnesota, USA | E-mail: stan.lipovetsky@gmail.com

Correspondence: [*] Corresponding author: Minneapolis, Minnesota, USA. E-mail: stan.lipovetsky@gmail.com

Abstract: The work presents various techniques of the logistic and multinomial-logit modeling with their modifications. These methods are useful for regression modeling with a binary or categorical outcome, structuring in regression and clustering, singular value decomposition and principal component analysis with positive loadings, and numerous other applications. Particularly, these models are employed in the discrete choice modeling and the best-worst scaling known in applied psychology and socio-economics studies.

Keywords: Logistic and MNL regression, cluster analysis, SVD, PCA, DCM, BWS

DOI: 10.3233/MAS-210543

Journal: Model Assisted Statistics and Applications, vol. 16, no. 4, pp. 279-282, 2021