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Model‐based small area estimates of overweight prevalence using sample selection adjustment
Author(s) -
Malec Donald,
Davis William W.,
Cao Xin
Publication year - 1999
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19991215)18:23<3189::aid-sim309>3.0.co;2-c
Subject(s) - overdispersion , national health and nutrition examination survey , statistics , overweight , hierarchical database model , cluster sampling , selection (genetic algorithm) , sampling (signal processing) , sampling design , small area estimation , sample size determination , model selection , multistage sampling , sample (material) , estimation , computer science , econometrics , multilevel model , cluster (spacecraft) , mathematics , medicine , environmental health , body mass index , data mining , count data , poisson distribution , population , machine learning , estimator , filter (signal processing) , chemistry , pathology , management , chromatography , computer vision , programming language , economics
Using a hierarchical model with an adjustment for sample selection, we estimate the overweight prevalence for adults, by states, using data from the Third National Health and Nutrition Examination Survey (NHANES III). A two‐stage hierarchical model was selected to account for geographic variability of outcomes and to model possible overdispersion of estimates due to cluster sampling. We compare our model‐based estimates with design‐based estimates at the national level and obtain excellent agreement. We also provide a check of our model at the state level by comparing estimates with design‐based and synthetic estimates. Copyright © 1999 John Wiley & Sons, Ltd.