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Quantile plots, generalized: update to Stata 7.0 [PDF]

Nicholas J. Cox
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Ordinal Logistic Regression Analysis in Effective Teaching Practices

Journal of Mathematics and Statistics
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Gene‐level association analysis of ordinal traits with functional ordinal logistic regressions

Genetic Epidemiology, 2022
AbstractIn this paper, we develop functional ordinal logistic regression (FOLR) models to perform gene‐based analysis of ordinal traits. In the proposed FOLR models, genetic variant data are viewed as stochastic functions of physical positions and the genetic effects are treated as a function of physical positions.
Chi‐Yang Chiu, Shuqi Wang, Bingsong Zhang, Yutong Luo, Claire Simpson, Wei Zhang, Alexander F. Wilson, Joan E. Bailey‐Wilson, Elvira Agron, Emily Y. Chew, Jun Zhang, Momiao Xiong, Ruzong Fan   +12 more
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Ordinal Logistic Regression

2010
In this chapter, the standard logistic model is extended to handle outcome variables that have more than two ordered categories. When the categories of the outcome variable have a natural order, ordinal logistic regression may be appropriate.
David G. Kleinbaum, Mitchel Klein
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Crash severity modelling using ordinal logistic regression approach

International Journal of Injury Control and Safety Promotion, 2020
Road traffic accident is one of the major problems facing the world. The carnage on Ghana's roads has raised road accidents to the status of a 'public health' threat. The objective of the study is to identify factors that contribute to accident severity using an ordinal regression model to fit a suitable model using the dataset extracted from the ...
Isaac Ofori Asare, Alice Constance Mensah   +1 more
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Classification Efficiency of Multinomial Logistic Regression Relative to Ordinal Logistic Regression

Journal of the American Statistical Association, 1989
Abstract Classification procedures are useful for the prediction of a response (or outcome) as a result of knowledge of the levels of one or more independent (or predictor) variables. The procedure is said to classify the (possibly multivariate) observation to a level of the response variable. An example might be the prediction of whether an individual
M. Karen Campbell, Allan Donner
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Ordinal logistic regression for affective product design

2008 IEEE International Conference on Industrial Engineering and Engineering Management, 2008
Affective product design, which focuses on customers? affective responses and aspirations, is arousing attention increasingly. This paper draws on ordinal logistic regression to deal with affective product design, mapping from designer domain to customer domain. It takes a designer?s perspective and facilitates the handling of affective information and
F. Zhou, D. Wu, X. Yang, J. Jiao
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Alternative models for ordinal logistic regression

Statistics in Medicine, 1994
AbstractArmstrong and Sloan have reviewed two types of ordinal logistic models for epidemiologic data: the cumulative‐odds model and the continuation‐ratio model. I review here certain aspects of these models not emphasized previously, and describe a third type, the stereotype model, which in certain situations offers greater flexibility coupled with ...
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A composite logistic regression approach for ordinal panel data regression

International Journal of Data Analysis Techniques and Strategies, 2008
We propose in this article a Composite Logistic Regression (CLR) approach for ordinal panel data regression. The new method transforms the original ordinal regression problem into a number of binary ones. Thereafter, the method of conditional logistic regression (Chamberlain, 1984; Wooldridge, 2001; Hsiao, 2003) can be directly applied.
Ronghua Luo, Hansheng Wang
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Non‐proportional odds multivariate logistic regression of ordinal family data

Biometrical Journal, 2014
Methods to examine whether genetic and/or environmental sources can account for the residual variation in ordinal family data usually assume proportional odds. However, standard software to fit the non‐proportional odds model to ordinal family data is limited because the correlation structure of family data is more complex than for other types of ...
Zaloumis, Sophie G., Scurrah, Katrina J., Harrap, Stephen B., Ellis, Justine A., Gurrin, Lyle C.   +4 more
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