On guaranteed parameter estimation of a multiparameter linear regression process
✍ Scribed by Uwe Küchler; Vyacheslav A. Vasiliev
- Publisher
- Elsevier Science
- Year
- 2010
- Tongue
- English
- Weight
- 506 KB
- Volume
- 46
- Category
- Article
- ISSN
- 0005-1098
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Liang and Zeger introduced a class of estimating equations that gives consistent estimates of regression parameters and of their variances in the class of generalized linear models for longitudinal data. When the response variable in such models is subject to overdispersion, the oerdispersion parame
## Abstract Consider the two linear regression models of __Y__~__ij__~ on __X__~__ij__~, namely __Y__~__ij__~ = β~__io__~ + β~__ij__~, __X__~__ij__~ + __E__~__ij__~ = 1, 2,…, __n__~__i__~, __i__ = 1, 2, where __E__~__ij__~ are assumed to be normally distributed with zero mean and common unknown var