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Non-Parametric Small Area Estimation Using Penalized Spline Regression
Open accessAbstract
Summary: The paper proposes a small area estimation approach that combines small area random effects with a smooth, non-parametrically specified trend. By using penalized splines as the representation for the non-parametric trend, it is possible to express the non-parametric small area estimation problem as a mixed effect model regression. The resulting model is readily fitted by using existing model fitting approaches such as restricted maximum likelihood. We present theoretical results on the prediction mean-squared error of the estimator proposed and on likelihood ratio tests for random effects, and we propose a simple non-parametric bootstrap approach for model inference and estimation of the small area prediction mean-squared error. The applicability of the method is demonstrated on a survey of lakes in north-eastern USA.
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Citations
Cited by
Mixed model prediction and small area estimation
Jiang, J., & Lahiri, P. (2006) · TEST · doi:10.1007/bf02595419 · Cited by 327
Keiding, N., & Louis, T. A. (2018) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-031017-100127 · Cited by 36
Metadata
- Authors
- Opsomer, J. D., Claeskens, G., Ranalli, M. G., Kauermann, G., & Breidt, F. J.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2008-01-04
- Volume
- 70
- Issue
- 1
- Pages
- 265-286
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W1813834185
- Cited by
- 196
- References
- 0
- FWCI
- 12.42
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: Non-Parametric Small Area Estimation Using Penalized Spline Regression. https://app.cochat.ai/papers/10.1111/j.1467-9868.2007.00635.x
- The paper
- Opsomer, J. D., Claeskens, G., Ranalli, M. G., Kauermann, G., & Breidt, F. J. (2008). Non-Parametric Small Area Estimation Using Penalized Spline Regression. Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1111/j.1467-9868.2007.00635.x
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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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