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https://doi.org/10.1016/j.csda.2006.01.012

has been cited by the following article:

• TITLE: Constructing Statistical Intervals for Small Area Estimates Based on Generalized Linear Mixed Model in Health Surveys

  AUTHORS: Yan Wang, Xingyou Zhang, Hua Lu, Janet B. Croft, Kurt J. Greenlund

  KEYWORDS: Bayesian Estimation, Behavioral Risk Factor Surveillance System, Bootstrapping, Monte Carlo Simulation, Small Area Estimation

  JOURNAL NAME: Open Journal of Statistics, Vol.12 No.1, February 14, 2022

  ABSTRACT: Generalized Linear Mixed Model (GLMM) has been widely used in small area estimation for health indicators. Bayesian estimation is usually used to construct statistical intervals, however, its computational intensity is a big challenge for large complex surveys. Frequentist approaches, such as bootstrapping, and Monte Carlo (MC) simulation, are also applied but not evaluated in terms of the interval magnitude, width, and the computational time consumed. The 2013 Florida Behavioral Risk Factor Surveillance System data was used as a case study. County-level estimated prevalence of three health-related outcomes was obtained through a GLMM; and their 95% confidence intervals (CIs) were generated from bootstrapping and MC simulation. The intervals were compared to 95% credential intervals through a hierarchial Bayesian model. The results showed that 95% CIs for county-level estimates of each outcome by using MC simulation were similar to the 95% credible intervals generated by Bayesian estimation and were the most computationally efficient. It could be a viable option for constructing statistical intervals for small area estimation in public health practice.