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- Brännäs, Kurt, et al.
(författare)
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Time-series count data regression
- 1994
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Ingår i: Communications in Statistics - Theory and Methods. - : Informa UK Limited. - 0361-0926 .- 1532-415X. ; 23:10, s. 2907-2925
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Tidskriftsartikel (refereegranskat)abstract
- The count data model studied in the paper extends the Poisson model by allowing for overdispersion and serial correlation. Alternative approaches to estimate nuisance parameters, required for the correction of the Poisson maximum likelihood covariance matrix estimator and for a quasi-likelihood estimator, are studied. The estimators are evaluated by finite sample Monte Carlo experimentation. It is found that the Poisson maximum likelihood estimator with corrected covariance matrix estimators provide reliable inferences for longer time series. Overdispersion test statistics are wellbehaved, while conventional portmanteau statistics for white noise have too large sizes. Two empirical illustrations are included.
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| 2. |
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| 3. |
- Segerstedt, Bo
(författare)
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On ordinary ridge regression in generalized linear models
- 1992
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Ingår i: Communications in Statistics - Theory and Methods. - Philadelphia : Taylor & Francis. - 0361-0926 .- 1532-415X. ; 21:8, s. 2227-2246
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Tidskriftsartikel (refereegranskat)abstract
- In this paper it is shown that an ill-conditioned data matrix has similar effects on the parameter estimator when estimating generalized linear models as when estimating linear regression models. Asymptotically, the average length of the maximum likelihood estimator of a parameter vector increases as the conditioning of the covariance matrix deteriorates. A generalization of the ridge regression is suggested for maximum likelihood estimation in generalized linear models. In particular the existence of a ridge coefficient, k, such that the asymptotic mean square error of the generalized linear model ridge estimator is smaller than the asymptotic variance of the maximum likelihood estimator is shown. A numerical example illustrates the theoretical results
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