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A new class of semi-mixed effects models and its application in small area estimation

Listed author(s):
  • José Lombardía, María
  • Sperlich, Stefan

In multi-level regression, using a fixed effect for each cluster leads to models that are flexible but that have poor estimation accuracy. In small area studies, for example, fixed effects models are typically over-parameterized. Regarding region as a random effect reduces the number of parameters, and hence, the flexibility, but requires crucial assumptions, such as that of independence between covariates and the random effects. A new class of semi-mixed effects models introduced here includes random and fixed effects models as extreme cases. This class of models constitutes a continuum of models, indexed by a “slider”, that determines the position of the model between these two extremes. Thus, the model selected can be close to the parsimonious random effects case, but far enough away from it to filter out unwanted dependences. The methodology is used for a small area analysis of tourist expenditures in Galicia.

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Article provided by Elsevier in its journal Computational Statistics & Data Analysis.

Volume (Year): 56 (2012)
Issue (Month): 10 ()
Pages: 2903-2917

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Handle: RePEc:eee:csdana:v:56:y:2012:i:10:p:2903-2917
DOI: 10.1016/j.csda.2012.01.015
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  1. Alessandro Tarozzi & Angus Deaton, 2009. "Using Census and Survey Data to Estimate Poverty and Inequality for Small Areas," The Review of Economics and Statistics, MIT Press, vol. 91(4), pages 773-792, November.
  2. Wangli Xu & Lixing Zhu, 2009. "Kernel-based Generalized Cross-validation in Non-parametric Mixed-effect Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 229-247.
  3. Chris Elbers & Jean O. Lanjouw & Peter Lanjouw, 2003. "Micro--Level Estimation of Poverty and Inequality," Econometrica, Econometric Society, vol. 71(1), pages 355-364, January.
  4. Jiming Jiang & P. Lahiri, 2006. "Mixed model prediction and small area estimation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 15(1), pages 1-96, June.
  5. Hausman, Jerry, 2015. "Specification tests in econometrics," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 38(2), pages 112-134.
  6. Naisyin Wang & Raymond J. Carroll & Xihong Lin, 2005. "Efficient Semiparametric Marginal Estimation for Longitudinal/Clustered Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 147-157, March.
  7. Gabriel DEMOMBYNES & Chris ELBERS & Jean O. LANJOUW & Peter LANJOUW, 2008. "How Good is a Map? Putting Small Area Estimation to the Test," Rivista Internazionale di Scienze Sociali, Vita e Pensiero, Pubblicazioni dell'Universita' Cattolica del Sacro Cuore, vol. 116(4), pages 465-493.
  8. R. Crouchley & R. B. Davies, 1999. "A comparison of population average and random-effect models for the analysis of longitudinal count data with base-line information," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(3), pages 331-347.
  9. María José Lombardía & Stefan Sperlich, 2008. "Semiparametric inference in generalized mixed effects models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(5), pages 913-930.
  10. Xihong Lin & Raymond J. Carroll, 2006. "Semiparametric estimation in general repeated measures problems," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(1), pages 69-88.
  11. J. D. Opsomer & G. Claeskens & M. G. Ranalli & G. Kauermann & F. J. Breidt, 2008. "Non-parametric small area estimation using penalized spline regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 265-286.
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