Data thinning splits area-level observations to enable out-of-sample validation of Fay-Herriot models, with recommendations for thinning parameters that balance bias and variance for stable model comparison.
Stat , volume = 11, number = 1, pages =
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The authors introduce Survey-aware Machine Learning (SaML) as a nine-step guideline that integrates survey design metadata throughout the ML lifecycle to enable valid population inference from complex health surveys.
citing papers explorer
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On Data Thinning for Model Validation in Small Area Estimation
Data thinning splits area-level observations to enable out-of-sample validation of Fay-Herriot models, with recommendations for thinning parameters that balance bias and variance for stable model comparison.
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Survey-aware Machine Learning: A Guideline for Valid Population Health Inference based on Scoping Review
The authors introduce Survey-aware Machine Learning (SaML) as a nine-step guideline that integrates survey design metadata throughout the ML lifecycle to enable valid population inference from complex health surveys.