Population-averaged and subject-specific approaches for clustered categorical data
- Author
- SCHABENBERGER, O1 ; GREGOIRE, T. G
[1] Statistics Collaborative, Inc. 1710 Rhode Island Ave. NW, Suite 200, Washington, DC 20036-3007, United States - Source
Journal of statistical computation and simulation (Print). 1996, Vol 54, Num 1-3, pp 231-253 ; ref : 1 p.3/4
- CODEN
- JSCSAJ
- ISSN
- 0094-9655
- Scientific domain
- Control theory, operational research; Mathematics
- Publisher
- Taylor and Francis, Abingdon
- Publication country
- United Kingdom
- Document type
- Article
- Language
- English
- Keyword (fr)
- Algorithme Corrélation Donnée catégorielle Implémentation Modèle linéaire généralisé Modèle mixte Nomenclature Equation estimante Pseudo vraisemblance Vraisemblance
- Keyword (en)
- Algorithm Correlation Categorical data Implementation Generalized linear model Mixed model Nomenclature Likelihood
- Keyword (es)
- Algoritmo Correlación Dato categórico Ejecución Modelo lineal generalizado Modelo mixto Nomenclatura Verosimilitud
- Classification
- Pascal
- 001 Exact sciences and technology / 001A Sciences and techniques of general use / 001A02 Mathematics / 001A02H Probability and statistics / 001A02H02 Statistics / 001A02H02I Multivariate analysis
- Pascal
- 001 Exact sciences and technology / 001A Sciences and techniques of general use / 001A02 Mathematics / 001A02H Probability and statistics / 001A02H02 Statistics / 001A02H02J Linear inference, regression
- Discipline
- Mathematics
- Origin
- Inist-CNRS
- Database
- PASCAL
- INIST identifier
- 3105616
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
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DOI
10.1080/00949659608811730 
https://dx.doi.org/10.1080/00949659608811730
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