Although generalized linear models are reasonably well known, they are not as widely used in medical statistics as might be appropriate, with the exception of logistic, log-linear, and some survival models. At the same time, the generalized linear modelling methodology is decidedly outdated in that
The generalized linear model and extensions: a review and some biological and environmental applications
β Scribed by Sudhir Paul; Krishna K. Saha
- Publisher
- John Wiley and Sons
- Year
- 2007
- Tongue
- English
- Weight
- 311 KB
- Volume
- 18
- Category
- Article
- ISSN
- 1180-4009
- DOI
- 10.1002/env.849
No coin nor oath required. For personal study only.
β¦ Synopsis
Abstract
The generalized linear model (GLM) encompasses many discrete and continuous models and it is particularly useful for analyzing discrete data. However, in many real life applications, the full distributional assumption of the GLM cannot be justified. Further, the GLM cannot accommodate overβdispersion in the data. Since the inception of the GLM by Nelder and Wedderburn (1972) a number of its extensions have been proposed in the literature for robust analysis of discrete data. The purpose of this paper is to critically review these extensions. Applications to overβdispersed Poisson and binomial models are shown. Some simulations are conducted to compare, in terms of bias and efficiency, the estimates of mean and the dispersion parameters by different methods. Applications to some biological and environmental data are given. Copyright Β© 2007 John Wiley & Sons, Ltd.
π SIMILAR VOLUMES
We calibrate and contrast the recent generalized multinomial logit model and the widely used latent class logit model approaches for studying heterogeneity in consumer purchases. We estimate the parameters of the models on panel data of household ketchup purchases, and find that the generalized mult