Ordered Logistic Regression Algorithm
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An Ordered Logistic Regression Algorithm is a ordered regression algorithm that is a logistic regression model.
References
2021
- (Wikipedia, 2021) ⇒ https://en.wikipedia.org/wiki/Ordered_logit Retrieved:2021-11-29.
- In statistics, the ordered logit model (also ordered logistic regression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent variables—first considered by Peter McCullagh. For example, if one question on a survey is to be answered by a choice among "poor", "fair", "good", and "excellent", and the purpose of the analysis is to see how well that response can be predicted by the responses to other questions, some of which may be quantitative, then ordered logistic regression may be used. It can be thought of as an extension of the logistic regression model that applies to dichotomous dependent variables, allowing for more than two (ordered) response categories.
2006
- (Williams, 2006) ⇒ Richard Williams. (2006). “Generalized Ordered Logit / Partial Proportional Odds Models for Ordinal Dependent Variables." Stata Journal 6, no. 1