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A predictive approach to the random effect model
Open accessAbstract
Two simple estimators are derived here for the means of the random effect model by means of predictive sample reuse. They are applied to two sets of data in the literature and compared with several other procedures. The mixed model is also discussed.
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Cited by
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Pls-Sem: indeed a silver bullet
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011) · Journal of Marketing Theory and Practice · doi:10.2753/mtp1069-6679190202 · Cited by 21,631
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Cross validatory choice and assessment of statistical predictions
Stone, M. (1974) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1974.tb00994.x · Cited by 10,881
An assessment of the use of partial least squares structural equation modeling in marketing research
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Metadata
- Authors
- GEISSER, S.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1974
- Volume
- 61
- Issue
- 1
- Pages
- 101-107
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W2066090712
- Cited by
- 2,961
- References
- 0
- FWCI
- 7.03
- Type
- journal article
- Language
- en
- Open access
- green
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- GEISSER, S. (1974). A predictive approach to the random effect model. Biometrika. https://doi.org/10.1093/biomet/61.1.101
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
