The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Misspecified maximum likelihood estimates and generalised linear mixed models
Abstract
We investigate the impact of model violations on the estimate of a regression coefficient in a generalised linear mixed model. Specifically, we evaluate the asymptotic relative bias that results from incorrect assumptions regarding the random effects. We compare the impact of model violation for two parameterisations of the regression model. Substantial bias in the conditionally specified regression point estimators can result from using a simple random intercepts model when either the random effects distribution depends on measured covariates or there are autoregressive random effects. A marginally specified regression structure that is estimated using maximum likelihood is much less susceptible to bias resulting from random effects model misspecification.
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Citations
Cites
Longitudinal data analysis using generalized linear models
LIANG, K.-Y., & ZEGER, S. L. (1986) · Biometrika · doi:10.1093/biomet/73.1.13 · Cited by 18,550
(1967) · Project Euclid (Cornell University) · Cited by 4,720
Approximate Inference in Generalized Linear Mixed Models
Breslow, N. E., & Clayton, D. G. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10594284 · Cited by 4,629
Models for Longitudinal Data: A Generalized Estimating Equation Approach
Zeger, S. L., Liang, K.-Y., & Albert, P. S. (1988) · Biometrics · doi:10.2307/2531734 · Cited by 4,505
Maximum Likelihood Estimation of Misspecified Models
White, H. (1983) · Econometrica · doi:10.2307/1912004 · Cited by 3,674
Random-Effects Models for Serial Observations with Binary Response
Stiratelli, R., Laird, N., & Ware, J. H. (1984) · Biometrics · doi:10.2307/2531147 · Cited by 733
A likelihood-based method for analysing longitudinal binary responses
FITZMAURICE, G. M., & LAIRD, N. M. (1993) · Biometrika · doi:10.1093/biomet/80.1.141 · Cited by 328
Heagerty, P. J., & Zeger, S. L. (2000) · Statistical Science · doi:10.1214/ss/1009212671 · Cited by 298
Self-citationMarginally Specified Logistic‐Normal Models for Longitudinal Binary Data
Heagerty, P. J. (1999) · Biometrics · doi:10.1111/j.0006-341x.1999.00688.x · Cited by 209
Self-citationThe effects of mixture distribution misspecification when fitting mixed-effects logistic models
NEUHAUS, J. M., HAUCK, W. W., & KALBFLEISCH, J. D. (1992) · Biometrika · doi:10.1093/biomet/79.4.755 · Cited by 189
Cited by
Robustness of linear mixed-effects models to violations of distributional assumptions
Schielzeth, H., Dingemanse, N. J., Nakagawa, S., Westneat, D. F., Allegue, H., Teplitsky, C., Réale, D., Dochtermann, N. A., Garamszegi, L. Z., & Araya‐Ajoy, Y. G. (2020) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13434 · Cited by 1,393
“For generalized linear mixed-effects models, the patterns have been more variable and fixed effect estimates can be biased in some cases (Grilli & Rampichini, 2015; Heagerty & Kurland, 2001).”
BackgroundFixed and random effects models: making an informed choice
Bell, A., Fairbrother, M., & Jones, K. (2019) · Quality & Quantity · doi:10.1007/s11135-018-0802-x · Cited by 1,085
Intermediate and advanced topics in multilevel logistic regression analysis
Austin, P. C., & Merlo, J. (2017) · Statistics in Medicine · doi:10.1002/sim.7336 · Cited by 649
Bayesian Multilevel Analysis and MCMC
Draper, D. (2007) · Handbook of Multilevel Analysis · doi:10.1007/978-0-387-73186-5_2 · Cited by 69
Metadata
- Authors
- Heagerty, P. J., & Kurland, B. F.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 2001-12-01
- Volume
- 88
- Issue
- 4
- Pages
- 973-985
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2094400652
- Cited by
- 294
- References
- 14
- FWCI
- 4.91
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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