Empirical best prediction for small-area inference based on generalized linear mixed models

By Jiming Jiang. 2003

70 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cf8a18e9b8b79bee970a71afb6a5d68dfa933ce5

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Citations in the corpus

Mixed model prediction and small area estimation (2006, methodology): “Jiang (2003) developed a method based on Taylor series expansion that gives an estimate whose bias is corrected to the second-order.”
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).”
Small area estimation of poverty proportions under unit-level temporal binomial-logit mixed models (2017, unknown): “The statistical methodology is taken and adapted from Jiang and Lahiri (2001) and Jiang (2003), where EBPs of functions of fixed effects and small area specific random effects were developed in the context of logisticmixedmodels andGLMM, respectively.”
On parametric bootstrap methods for small area prediction (2006, methodology): “Jiang (2003) gave a more extensive review of Bayesian approaches and developed frequentist, although not bootstrap, methodology which parallels existing hierarchical Bayes methods.”
Empirical Best Prediction Under Unit-Level Logit Mixed Models (2016, methodology): “In particular, some properties established by Jiang (1998, 2003) and Jiang and Lahiri (2001) are employed in the article to derive the approximation to the model-based MSE of the EBP, conditional on a specified model and without random effects at levels finer than domain.”
Empirical best prediction under area-level Poisson mixed models (2016, methodology): “The statistical methodology is taken and adapted from Jiang and Lahiri (2001) and Jiang (2003), where EBPs of functions of fixed effects and small area specific random effects were developed in the context of logistic mixed models and GLMM respectively.”
Estimation of Finite Population Domain Means (2006, methodology): “Jiang and Lahiri (2001) considered an EBP of an arbitrary function of the regression coefficient β and the random effect vi.”
Semiparametric inference in generalized mixed effects models (2008, methodology): “Malec, Sedransk, Moriarity, and LeClere 1997; Ghosh, Natarajan, Stroud, and Carlin, 1998; Butar and Lahiri, 2003) or using frequentist methodology (Prasad and Rao, 1990; Jiang and Lahiri, 2001; Jiang, 2003; Das, Sedransk, Moriarity, and LeClere, 2004; González-Manteiga, Lombard́ıa, Molina, Morales, ”