Combining information from multiple surveys through the empirical likelihood method
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Publication:4818484
DOI10.2307/3315996zbMath1054.62009OpenAlexW2009378006MaRDI QIDQ4818484
Publication date: 28 September 2004
Published in: Canadian Journal of Statistics (Search for Journal in Brave)
Full work available at URL: https://semanticscholar.org/paper/c5d6d77ea515a9b6001c3c2cc4ee0f46679504ca
simulationsNewton-Raphson algorithmgeneralized regression estimatorpseudo empirical likelihoodconsistency requirementBenchmark constraint
Related Items (11)
Statistical data integration in survey sampling: a review ⋮ Small area estimation combining data from two surveys ⋮ Pseudo empirical likelihood inference for nonprobability survey samples ⋮ Empirical Likelihood Approach for Aligning Information from Multiple Surveys ⋮ Design‐based Estimators Calibrated on Estimated Totals from Multiple Surveys ⋮ A measurement error model approach to survey data integration: combining information from two surveys ⋮ Combining Data from New and Traditional Sources in Population Surveys ⋮ Statistical matching and uncertainty analysis in combining household income and expenditure data ⋮ Doubly Robust Regression Analysis for Data Fusion ⋮ Enriching Surveys with Supplementary Data and its Application to Studying Wage Regression ⋮ Empirical likelihood inference for a common mean in the presence of heteroscedasticity
Cites Work
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- Empirical likelihood ratio confidence regions
- Calibration Estimators in Survey Sampling
- Aligning Estimates for Common Variables in Two or More Sample Surveys
- Using empirical likelihood methods to obtain range restricted weights in regression estimators for surveys
- Survey Design Under the Regression Superpopulation Model
- A new estimation theory for sample surveys
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