The robustness of the quasilikelihood estimator
- 1 June 1999
- journal article
- Published by Wiley in The Canadian Journal of Statistics / La Revue Canadienne de Statistique
- Vol. 27 (2) , 321-327
- https://doi.org/10.2307/3315642
Abstract
The quasilikelihood estimator is widely used in data analysis where a likelihood is not available. We illustrate that with a given variance function it is not only conservative, in minimizing a maximum risk, but also robust against a possible misspecification of either the likelihood or cumulants of the model. In examples it is compared with estimators based on maximum likelihood and quadratic estimating functions.Keywords
This publication has 10 references indexed in Scilit:
- Likelihood, Quasi-Likelihood and Pseudolikelihood: Some ComparisonsJournal of the Royal Statistical Society Series B: Statistical Methodology, 1992
- An extension of quasi-likelihood estimationJournal of Statistical Planning and Inference, 1989
- Generalized Linear ModelsPublished by Springer Nature ,1989
- Multiplicative Errors: Log-Normal or Gamma?Journal of the Royal Statistical Society Series B: Statistical Methodology, 1988
- On linear and quadratic estimating functionsBiometrika, 1987
- An extended quasi-likelihood functionBiometrika, 1987
- On the efficiency of quasi-likelihood estimationBiometrika, 1987
- On Consistency and Inconsistency of Estimating EquationsEconometric Theory, 1986
- Quasi-Likelihood FunctionsThe Annals of Statistics, 1983
- A Comparison between Maximum Likelihood and Generalized Least Squares in a Heteroscedastic Linear ModelJournal of the American Statistical Association, 1982