A Bayesian predictive inference for small area means incorporating covariates and sampling weights

DOI10.1016/j.jspi.2010.03.043zbMath1203.62029OpenAlexW2082642613MaRDI QIDQ988923

Balgobin Nandram, Ma. Criselda S. Toto

Publication date: 19 August 2010

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2010.03.043

zbMATH Keywords

random samplesposterior proprietysmall area estimationsampling weightsmultivariate normal densitynested-error regression model

Mathematics Subject Classification ID

Related Items (4)

Small area estimation of general parameters with application to poverty indicators: a hierarchical Bayes approach ⋮ Benchmarking techniques for reconciling Bayesian small area models at distinct geographic levels ⋮ Bayesian predictive inference under a Dirichlet process with sensitivity to the normal baseline ⋮ Bayesian benchmarking with applications to small area estimation

Cites Work

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• Bayesian benchmarking with applications to small area estimation
• Pseudo hierarchical Bayes small area estimation combining unit level models and survey weights
• Small area estimation using unmatched sampling and linking models
• A pseudo-empirical best linear unbiased prediction approach to small area estimation using survey weights
• A Bayesian benchmarking of the Scott–Smith model for small areas

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