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- W1749277572 abstract "The hierarchical Bayes (HB) approach based on Markov chain Monte Carlo (MCMC) has several limitations, so it is important to exercise caution when implementing MCMC. This chapter gives a brief account of some practical issues associated with MCMC. The HB approach is applied to the basic area level model, basic unit level model with equal error variances, and the general ANOVA model, assuming a prior distribution on the model parameters. The HB approach is a good alternative to the empirical Bayes (EB) method for the estimation of general finite population quantities because it avoids the use of the bootstrap for mean squared error (MSE) estimation. The chapter then focuses on three different HB models, assuming priors on the model parameters. Finally, it discusses HB estimation for Poisson-gamma, log-normal, and CAR-normal models, assuming priors on the model parameters, also considering extensions to two-level models." @default.
- W1749277572 created "2016-06-24" @default.
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- W1749277572 date "2015-08-14" @default.
- W1749277572 modified "2023-10-16" @default.
- W1749277572 title "Hierarchical Bayes (HB) Method" @default.
- W1749277572 doi "https://doi.org/10.1002/9781118735855.ch10" @default.
- W1749277572 hasPublicationYear "2015" @default.
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