High-Dimensional Multivariate Probit Analysis
Copyright policy )High-Dimensional Multivariate Probit Analysis
A computationally practical form of probit analysis for multiple response variables based on an assumed common factor model for the latent tolerances is proposed. Numerical integration over the factor space provides maximum likelihood estimation of the probit regression parameters and of the probabilities of response combinations under the model. The procedure is applied to five variables from the Pneumoconiosis Field Trial, two variables of which were previously analyzed by Ashford and Sowden (1970, Biometrics 26, 535-546).
- University of Chicago United States
- University of Illinois at Chicago United States
Generalized linear models (logistic models), Biometry, Models, Statistical, Estimation in multivariate analysis, Multivariate Analysis, Humans, Pneumoconiosis, Algorithms
Generalized linear models (logistic models), Biometry, Models, Statistical, Estimation in multivariate analysis, Multivariate Analysis, Humans, Pneumoconiosis, Algorithms
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).56 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!