Consistent Estimators in Generalized Linear Mixed Models
Abstract
A simple method based on simulated moments is proposed for estimating the fixed-effects and variance components in a generalized linear mixed model (GLMM). It is shown that the method is not only computationally attractive but also leads to consistent estimators. On the other hand, simulation shows that the method can be quite inefficient.
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Mixed models have consistent estimators and do not require balanced data.
“In imbalanced situations, “pseudo-bulk” methods also cause cells from individuals with fewer cells to be more heavily weighted, where mixed models have consistent estimators and do not require balanced data29.”
From A practical solution to pseudoreplication bias in single-cell studies (2021) · cites this paper as [29]
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Self-citation
- Authors
- Jiang, J.
- Affiliations
- Case Western Reserve University (US)
- Corresponding author
- Jiang, Jiming
- ORCID
- Jiang, Jiming
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 1998-06
- Volume
- 93
- Issue
- 442
- Pages
- 720-729
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W2132137727
- Cited by
- 149
- References
- 24
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 3 · 2022: 5 · 2021: 13 · 2020: 3 · 2019: 2 · 2018: 3 · 2017: 4 · 2016: 4 · 2015: 5 · 2014: 4 · 2013: 4 · 2012: 5
- FWCI
- 5.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of the American Statistical Association (publishedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Markov Chains and Monte Carlo Methods
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Keywords
- generalized linear mixed models, consistent estimators, fixed effects estimation, variance component estimation, computational efficiency
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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