Response models for mixed binary and quantitative variables

SUMMARY A number of special representations are considered for the joint distribution of qualitative, mostly binary, and quantitative variables. In addition to the conditional Gaussian models and to conditional Gaussian regression chain models some emphasis is placed on models derived from an underl...

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Bibliographic Details
Published in:Biometrika 1992-09, Vol.79 (3), p.441-461
Main Authors: COX, D. R., WERMUTH, NANNY
Format: Article
Language:English
Subjects:
Approximation
Conditional Gaussian model
Conditional probabilities
Exact sciences and technology
Gaussian distributions
Graphical chain model
Least squares
Linear inference, regression
Linear model
Linear models
Linear regression
Logistic function
Logistics
Mathematics
Multivariate normal distribution
Null hypothesis
Probability and statistics
Probit model
Regression analysis
Sciences and techniques of general use
Statistical discrepancies
Statistics
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Summary:SUMMARY A number of special representations are considered for the joint distribution of qualitative, mostly binary, and quantitative variables. In addition to the conditional Gaussian models and to conditional Gaussian regression chain models some emphasis is placed on models derived from an underlying multivariate normal distribution and on models in which discrete probabilities are specified linearly in terms of unknown parameters. The possibilities for choosing between the models empirically are examined, as well as the testing of independence and conditional independence and the estimation of parameters. Often the testing of independence is exactly or nearly the same for a number of different models.
ISSN:0006-3444
1464-3510
DOI:10.1093/biomet/79.3.441