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
Bayesian inference in joint modelling of location and scale parameters of the t distribution for longitudinal data
โ Scribed by Tsung-I Lin; Wan-Lun Wang
- Book ID
- 108193508
- Publisher
- Elsevier Science
- Year
- 2011
- Tongue
- English
- Weight
- 309 KB
- Volume
- 141
- Category
- Article
- ISSN
- 0378-3758
No coin nor oath required. For personal study only.
๐ SIMILAR VOLUMES
We are considering the ABLUE's -asymptotic best linear unbiased estimators -of the location parameter and the scale parameter of the population jointly based on a set of selected k sample quantiles, when the population distribution has the density of the form
We explore the effects of measurement error in a time-varying covariate for a mixed model applied to a longitudinal study of plasma levels and dietary intake of beta-carotene. We derive a simple expression for the bias of large sample estimates of the variance of random effects in a longitudinal mod