Skip Navigation

Biometrika 1992 79(3):441-461; doi:10.1093/biomet/79.3.441
© 1992 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by COX, D. R.
Right arrow Articles by WERMUTH, N.
Right arrow Search for Related Content
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

Response models for mixed binary and quantitative variables

D. R. COX1 and NANNY WERMUTH2

1Nuffield College Oxford OX1 1NF, U.K.
2Psychological Institute, University of Mainz 6500 Mainz, Germany

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.

Key Words: Conditional Gaussian model • Graphical chain model • Linear model • Logistic function • Multivariate normal distribution • Probit model


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
 Stat Methods Med ResHome page
N. J Horton and N. M Laird
Maximum likelihood analysis of generalized linear models with missing covariates
Statistical Methods in Medical Research, February 1, 1999; 8(1): 37 - 50.
[Abstract] [PDF]


Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.