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Generalized Linear Models
Abstract
The technique of iterative weighted linear regression can be used to obtain maximum likelihood estimates of the parameters with observations distributed according to some exponential family and systematic effects that can be made linear by a suitable transformation. A generalization of the analysis of variance is given for these models using log-likelihoods. These generalized linear models are illustrated by examples relating to four distributions; the Normal, Binomial (probit analysis, etc.), Poisson (contingency tables) and gamma (variance components). The implications of the approach in designing statistics courses are discussed.
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Citations
Cited by
(2005) · Statistics for Biology and Health · doi:10.1007/0-387-27255-0_9 · Cited by 20,133
McCarthy, D. J., Chen, Y., & Smyth, G. K. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks042 · Cited by 5,195
Regression Models for Ordinal Data
McCullagh, P. (1980) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1980.tb01109.x · Cited by 4,453
Hastie, T., & Tibshirani, R. (1986) · Statistical Science · doi:10.1214/ss/1177013604 · Cited by 3,409
Econometric Models for Count Data with an Application to the Patents-R & D Relationship
Hausman, J., Hall, B. H., & Griliches, Z. (1984) · Econometrica · doi:10.2307/1911191 · Cited by 3,393
Metadata
- Authors
- Nelder, J. A., & Wedderburn, R. W. M.
- Venue
- Journal of the Royal Statistical Society. Series A (General)
- Publisher
- JSTOR
- Published
- 1972
- Volume
- 135
- Issue
- 3
- Pages
- 370
- ISSN
- 0035-9238 (ISSN-L)
- OpenAlex
- W2801490189
- Cited by
- 7,194
- References
- 0
- FWCI
- 7.79
- Type
- journal article
- Language
- en
- Open access
- closed
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