A UNIFIED MEASURE OF UNCERTAINTY OF ESTIMATED BEST LINEAR UNBIASED PREDICTORS IN SMALL AREA ESTIMATION PROBLEMS

By G. S. Datta, Partha Lahiri. 2000

348 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/3ec2481b3e707e31db84baafd24ad5b71221a0e1

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Citations in the corpus

Small Area Shrinkage Estimation (2012, methodology): “This happens for the ANOVA estimators suggested by Prasad and Rao (1990) and the REML estimators considered by Datta and Lahiri (2000).”
Non‐parametric small area estimation using penalized spline regression (2008, background): “Datta and Lahiri (2000) later extended their results for the case of restricted maximum likelihood estimation of the variance components, and Das et al. (2004) further expanded it to encompass more general linear mixed models.”
Small area estimation in the presence of correlated random area effects (2009, methodology): “…Restricted Maximum Likelihood (REML) methods, and g3i s 2 u ; r can be obtained by following the results of Kackar and Harville (1984), Prasad and Rao (1990), Datta and Lahiri (2000), Singh et al. (2005) and Pratesi and Salvati (2008); see Appendix A for details of the computation of g3i s 2 u ; r…”
Model Selection by Testing for the Presence of Small-Area Effects, and Application to Area-Level Data (2011, background): “See, for example, the work of Battese, Harter, and Fuller (1988), Prasad and Rao (1990), Datta and Ghosh (1991), Singh, Stukel, and Pfeffermann (1998), Datta and Lahiri (2000), Hall and Maiti (2006a, 2006b), and Chatterjee, Lahiri, and Li (2008).”
Nonparametric estimation of mean-squared prediction error in nested-error regression models (2005, unknown): “Further discussion of jackknife methods in the small-area estimation problem is given by Lahiri [23].”
Mean‐squared error estimation in transformed Fay–Herriot models (2006, methodology): “There has until now been no extension of the very useful theories of Prasad and Rao (1990) and Datta and Lahiri (2001) on ‘second-order correct’ estimation of MSEs of Fay–Herriot small”
Bootstrap mean squared error of a small-area EBLUP (2008, methodology): “Using similar ideas as in Butar and Lahiri [25] and Pfeffermann and Tiller [17], but applied to the bootstrap procedure for finite populations described above, a second bootstrap estimator of MSE(η̂E) is proposed.”
Conditional and Marginal Models: Another View (2004, methodology): “Roy. Statist. Soc. Ser. B 13 238–241. WEDDERBURN, R. W. M. (1974). Quasilikelihood functions, generalized linear models and the Gauss–Newton method. Biometrika 61 439–447. WEIL, C. S. (1970). Selection of the valid number of sampling units and a consideration of their combination in toxicological st”
Empirical best prediction under area-level Poisson mixed models (2016, methodology): “The MSE of the EBP is estimated by parametric bootstrap and the MSE of the EBLUP by the g1-g3 formula given by Datta and Lahiri (2000). Direct estimators of pd and of its design-based variance are calculated following (10), where N̂d = ∑ j∈sd wdj and the wdj ’s are the official calibrated SLCS sampl”
Best Predictive Small Area Estimation (2011, methodology): “The method has since been used extensively in SAE and several extensions have been given (e.g., Lahiri and Rao 1995; Datta and Lahiri 2000; Jiang and Lahiri 2001; Datta, Rao, and Smith 2005).”
On measures of uncertainty of empirical Bayes small-area estimators (2003, methodology): “See Datta and Lahiri (2000).”
On parametric bootstrap methods for small area prediction (2006, background): “The set-up is similar to that considered by Datta et al. (2005). Model M1 is nearly balanced, but model M2 has high variability.”
Mixed model prediction and small area estimation (2006, methodology): “Datta and Lahiri (2001) discussed a model selection method for choosing between a fixed effect model and a random effect model, a topic of interest in small area applications. Jiang and Rao (2003) proposed two methods for linear mixed model selection in general. The first method selects variables as”