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Communications in Mathematical Biology and Neuroscience ISSN 2052-2541

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Commun. Math. Biol. Neurosci. 2021 (2021), Article ID 28

Estimation of nonparametric ordinal logistic regression model using local maximum likelihood estimation

Marisa Rifada, Nur Chamidah, Vita Ratnasari, Purhadi -

PDF (421.9 KB) https://doi.org/10.28919/cmbn/5353

Abstract

Ordinal logistic regression is a statistical method used to analyze the ordinal response variable with three or more categories and predictor variables that are categorical or continuous. The parametric models for ordinal response variable assume that the predictor is given by a linear form of covariates. In this study, the parametric models are extended to include smooth components based on nonparametric approach. The covariates are modeled as unspecified but smooth functions. Estimation is based on local maximum likelihood estimation (LMLE).

Published
5 April 2021
Volume
2021 (2021)
License
CC BY 3.0

Copyright © 2021 Marisa Rifada, Nur Chamidah, Vita Ratnasari, Purhadi -. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Cite this article

M. Rifada, N. Chamidah, V. Ratnasari, P. -, Estimation of nonparametric ordinal logistic regression model using local maximum likelihood estimation, Commun. Math. Biol. Neurosci. 2021 (2021), 28. https://doi.org/10.28919/cmbn/5353

BibTeX
@article{Rifada2021,
  author  = {Rifada, Marisa and Chamidah, Nur and Ratnasari, Vita and -, Purhadi},
  title   = {Estimation of nonparametric ordinal logistic regression model using local maximum likelihood estimation},
  journal = {Commun. Math. Biol. Neurosci.},
  volume  = {2021},
  year    = {2021},
  pages   = {28},
  doi     = {10.28919/cmbn/5353}
}