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Mixed model prediction and small area estimation
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A derivation of BLUP—Best linear unbiased predictor
Jiang, J. (1997) · Statistics & Probability Letters · doi:10.1016/s0167-7152(96)00089-2 · Cited by 37
Self-citationTesting Multiple Dispersion Effects in Unreplicated Fractional Factorial Designs
McGrath, R. N., & Lin, D. K. J. (2001) · Technometrics · doi:10.1198/00401700152672492 · Cited by 36
A General Framework for Model-Based Statistics
Hill, J. R. (1990) · Biometrika · doi:10.1093/biomet/77.1.115 · Cited by 35
Empirical Bayes Estimation of Finite Population Means from Complex Surveys
Arora, V., Lahiri, P., & Mukherjee, K. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10473677 · Cited by 33
Self-citationA quasi-likelihood approach to the REML estimating equations
Heyde, C. C. (1994) · Statistics & Probability Letters · doi:10.1016/0167-7152(94)00035-2 · Cited by 31
Dempster, A. P., & RYAN, L. M. (1985) · Journal of the American Statistical Association · doi:10.1080/01621459.1985.10478193 · Cited by 30
Wald consistency and the method of sieves in REML estimation
Jiang, J. (1997) · The Annals of Statistics · doi:10.1214/aos/1031594742 · Cited by 29
Self-citationEmpirical Bayes prediction intervals in a normal regression model: higher order asymptotics
Basu, R., Ghosh, J. K., & Mukerjee, R. (2003) · Statistics & Probability Letters · doi:10.1016/s0167-7152(03)00083-x · Cited by 26
A Hierarchical Bayes Approach to Small Area Estimation with Auxiliary Information
Ghosh, M., & Lahiri, P. (1992) · Lecture notes in statistics · doi:10.1007/978-1-4612-2944-5_6 · Cited by 23
Self-citation(2001) · SPIE eBooks · doi:10.1214/lnms/1215540957 · Cited by 23
Restricted Maximum Likelihood ( REML )
Speed, T. P. (2005) · Wiley eBooks · doi:10.1002/0471667196.ess0337.pub2 · Cited by 19
A nonlinear Gauss-Seidel algorithm for inference about GLMM
Jiang, J. (2000) · Computational Statistics · doi:10.1007/s001800000030 · Cited by 17
Self-citationPartially observed information and inference about non-Gaussian mixed linear models
Jiang, J. (2005) · The Annals of Statistics · doi:10.1214/009053605000000543 · Cited by 16
Self-citationEmpirical method of moments and its applications
Jiang, J. (2003) · Journal of Statistical Planning and Inference · doi:10.1016/s0378-3758(02)00118-0 · Cited by 16
Self-citationPARTIALLY OBSERVED INFORMATION AND INFERENCE ABOUT NON-GAUSSIAN MIXED LINEAR MODELS1Preprint
Jiang, J. (2016) · Cited by 16
Self-citationEmpirical Bayes Estimation for the Finite Population Mean on the Current Occasion
Nandram, B., & Sedransk, J. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476367 · Cited by 13
EB and EBLUP in Small Area Estimation
Rao, J. N. K. (2001) · Lecture notes in statistics · doi:10.1007/978-1-4613-0141-7_3 · Cited by 12
INTERACTIONS BETWEEN SURVEY ESTIMATES AND FEDERAL FUNDING FORMULASPreprint
Zaslavsky, A. M., & Schirm, A. L. (2006) · RePEc: Research Papers in Economics · Cited by 10
On the consistency between model-and design-based estimators in survey sampling
Ghosh, M., & SINHA, B. K. (1990) · Communication in Statistics- Theory and Methods · doi:10.1080/03610929008830226 · Cited by 9
Calibrated Weighting for Small Area Estimation
Chambers, R. L. (2005) · ePrints Soton (University of Southampton) · Cited by 6
Dunson, D. B. (2005) · Handbook of statistics · doi:10.1016/s0169-7161(05)25025-3 · Cited by 6
Cited by
Rao, J. N. K. (2004) · Encyclopedia of Statistical Sciences · doi:10.1002/0471667196.ess0731 · Cited by 2,049
Metadata
- Authors
- Jiang, J., & Lahiri, P.
- Venue
- TEST
- Published
- 2006-06
- Volume
- 15
- Issue
- 1
- ISSN
- 1133-0686 (ISSN-L)
- 1863-8260
- OpenAlex
- W1991386268
- Cited by
- 327
- References
- 159
- FWCI
- 12.81
- Type
- journal article
- Language
- en
- Open access
- closed
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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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
