Combining survey and non-survey data for improved sub-area prediction using a multi-level model

DOI10.1007/s13253-018-0320-2zbMath1391.62277OpenAlexW2800611357WikidataQ60521608 ScholiaQ60521608MaRDI QIDQ725241

Jae Kwang Kim, Zhonglei Wang, Nathan B. Cruze, Zhengyuan Zhu

Publication date: 1 August 2018

Published in: Journal of Agricultural, Biological, and Environmental Statistics (Search for Journal in Brave)

Full work available at URL: https://lib.dr.iastate.edu/cgi/viewcontent.cgi?article=1145&context=stat_las_pubs

zbMATH Keywords

hierarchical modelsmall area estimationmean squared prediction erroragricultural surveysurvey integration

Mathematics Subject Classification ID

Applications of statistics to environmental and related topics (62P12) Sampling theory, sample surveys (62D05)

Related Items (1)

Inference with non-probability samples and survey data integration: a science mapping study

Cites Work

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• On small area estimation under a sub-area level model
• Resampling methods for spatial regression models under a class of stochastic designs
• On the convergence properties of the EM algorithm
• Bayesian prediction in linear models: Applications to small area estimation
• Small area estimation: an appraisal. With comments and a rejoinder by the authors
• Small area shrinkage estimation
• Small Area Estimation
• Combining data from two independent surveys: a model-assisted approach
• Generalized Linear Models for Small-Area Estimation
• Aligning Estimates for Common Variables in Two or More Sample Surveys
• Combining Information from Multiple Surveys by using Regression for Efficient Small Domain Estimation
• Combining Independent Regression Estimators From Multiple Surveys
• A pseudo-empirical best linear unbiased prediction approach to small area estimation using survey weights
• Small Area Estimation-New Developments and Directions
• Statistics for Spatial Data
• Combining Information From Two Surveys to Estimate County-Level Prevalence Rates of Cancer Risk Factors and Screening
• Obtaining Cancer Risk Factor Prevalence Estimates in Small Areas: Combining Data from Two Surveys
• On a Least Squares Adjustment of a Sampled Frequency Table When the Expected Marginal Totals are Known
• Big Data, Official Statistics and Some Initiatives by the Australian Bureau of Statistics
• Calibration Weighting Methods for Complex Surveys

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