What question did this study set out to answer?

To develop and evaluate a general class of regression models for ordinal responses that rely on stochastic ordering rather than arbitrary numerical score assignments.

January 1, 1980Journal of the Royal Statistical Society Series B (Statistical Methodology)

Regression Models for Ordinal Data

View Full Paper

Ask AI

Bookmark

Authors

PMPeter McCullaghRutgers, The State University of New Jersey

Discussion

Member takes

Implication

Methodological study demonstrates flexible regression modeling for ordinal response data, indicating that stochastic ordering eliminates arbitrary numerical scoring.

Key Points

To develop and evaluate a general class of regression models for ordinal responses that rely on stochastic ordering rather than arbitrary numerical score assignments.
Formulated mathematical models for ordinal categorical outcomes utilizing principles of stochastic ordering, focusing on proportional odds and proportional hazards structures.
Generalized linear models to multivariate ordinal frameworks and extended the parameterization to accommodate non-linear specifications.
Implemented iteratively reweighted least squares algorithms to calculate parameter estimates.
Demonstrated that proportional odds and proportional hazards models serve as multivariate extensions of generalized linear models with straightforward practical interpretability.
Showed that iteratively reweighted least squares consistently converges to maximum likelihood estimates across both linear and non-linear model formulations, streamlining computation.

Cite This Study

Peter McCullagh (1980) studied this question.

synapsesocial.com/papers/6a00ce03413f0c047f2d7f22 https://doi.org/10.1111/j.2517-6161.1980.tb01109.x

View Full Paper

Ask AI

Bookmark

January 1, 1980Journal of the Royal Statistical Society Series B (Statistical Methodology)

Regression Models for Ordinal Data

View Full Paper

Ask AI

Bookmark

Authors

PMPeter McCullaghRutgers, The State University of New Jersey

Discussion

Member takes

Implication

Methodological study demonstrates flexible regression modeling for ordinal response data, indicating that stochastic ordering eliminates arbitrary numerical scoring.

Key Points

To develop and evaluate a general class of regression models for ordinal responses that rely on stochastic ordering rather than arbitrary numerical score assignments.
Formulated mathematical models for ordinal categorical outcomes utilizing principles of stochastic ordering, focusing on proportional odds and proportional hazards structures.
Generalized linear models to multivariate ordinal frameworks and extended the parameterization to accommodate non-linear specifications.
Implemented iteratively reweighted least squares algorithms to calculate parameter estimates.
Demonstrated that proportional odds and proportional hazards models serve as multivariate extensions of generalized linear models with straightforward practical interpretability.
Showed that iteratively reweighted least squares consistently converges to maximum likelihood estimates across both linear and non-linear model formulations, streamlining computation.

Cite This Study

Peter McCullagh (1980) studied this question.

synapsesocial.com/papers/6a00ce03413f0c047f2d7f22 https://doi.org/10.1111/j.2517-6161.1980.tb01109.x

Also Consider

Synapse has enriched 3 closely related papers on similar clinical questions. Consider them for comparative context:

View Full Paper

Ask AI

Bookmark