hbsae: Hierarchical Bayesian Small Area Estimation
Functions to compute small area estimates based on a basic area or unit-level model. The model is fit using restricted maximum likelihood, or in a hierarchical Bayesian way. In the latter case numerical integration is used to average over the posterior density for the between-area variance. The output includes the model fit, small area estimates and corresponding MSEs, as well as some model selection measures. Additional functions provide means to compute aggregate estimates and MSEs, to minimally adjust the small area estimates to benchmarks at a higher aggregation level, and to graphically compare different sets of small area estimates.
- Harm Jan Boonstra
- Date of publication
- 2012-09-05 13:03:20
- Harm Jan Boonstra <firstname.lastname@example.org>
- Compute aggregates of small area estimates and MSEs.
- Benchmark small area estimates.
- Compute area-level cross-validation measure for sae objects.
- Fit a linear model with random area effects and compute small...
- Compute small area estimates based on the basic area-level...
- Compute small area estimates based on the basic unit-level...
- Compute small area estimates based on the survey regression...
- Generate artificial dataset for demonstration and testing...
- A package for hierarchical Bayesian small area estimation.
- Plot method for objects of class sae.
- Plot method for objects of class 'weights'.
- Print method for objects of class sae.
- S3 class for the fitted model and SAE outcomes.
- Summary method for objects of class 'weights'.
- Compute unit weights underlying the small area estimates or...
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