Package: saekernel 0.1.1
saekernel: Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel
Propose an area-level, non-parametric regression estimator based on Nadaraya-Watson kernel on small area mean. Adopt a two-stage estimation approach proposed by Prasad and Rao (1990). Mean Squared Error (MSE) estimators are not readily available, so resampling method that called bootstrap is applied. This package are based on the model proposed in Two stage non-parametric approach for small area estimation by Pushpal Mukhopadhyay and Tapabrata Maiti(2004) <http://www.asasrms.org/Proceedings/y2004/files/Jsm2004-000737.pdf>.
Authors:
saekernel_0.1.1.tar.gz
saekernel_0.1.1.zip(r-4.7)saekernel_0.1.1.zip(r-4.6)saekernel_0.1.1.zip(r-4.5)
saekernel_0.1.1.tgz(r-4.6-any)saekernel_0.1.1.tgz(r-4.5-any)
saekernel_0.1.1.tar.gz(r-4.6-any)saekernel_0.1.1.tar.gz(r-4.5-any)
saekernel_0.1.1.tgz(r-4.5-emscripten)
saekernel.pdf |saekernel.html✨
saekernel/json (API)
| # Install 'saekernel' in R: |
| install.packages('saekernel', repos = c('https://wicaksh.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/wicaksh/saekernel/issues
- Data_saekernel - Sample Data for Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel
Last updated from:683e46590e. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 108 | ||
| source / vignettes | OK | 160 | ||
| linux-release-x86_64 | OK | 110 | ||
| macos-release-arm64 | OK | 176 | ||
| macos-oldrel-arm64 | OK | 155 | ||
| windows-devel | OK | 94 | ||
| windows-release | OK | 68 | ||
| windows-oldrel | OK | 73 | ||
| wasm-release | OK | 87 |
Exports:mse_saekernelsaekernel
Dependencies:
