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:Wicak Surya Hasani[aut, cre], Azka Ubaidillah[aut]

saekernel_0.1.1.tar.gz
saekernel_0.1.1.zip(r-4.5)saekernel_0.1.1.zip(r-4.4)saekernel_0.1.1.zip(r-4.3)
saekernel_0.1.1.tgz(r-4.5-any)saekernel_0.1.1.tgz(r-4.4-any)saekernel_0.1.1.tgz(r-4.3-any)
saekernel_0.1.1.tar.gz(r-4.5-noble)saekernel_0.1.1.tar.gz(r-4.4-noble)
saekernel_0.1.1.tgz(r-4.4-emscripten)saekernel_0.1.1.tgz(r-4.3-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

Datasets:
  • Data_saekernel - Sample Data for Small Area Estimation Non-Parametric Based Nadaraya-Watson Kernel

On CRAN:

Conda:

3.70 score 2 scripts 198 downloads 2 exports 0 dependencies

Last updated 4 years agofrom:683e46590e. Checks:9 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 09 2025
R-4.5-winOKMar 09 2025
R-4.5-macOKMar 09 2025
R-4.5-linuxOKMar 09 2025
R-4.4-winOKMar 09 2025
R-4.4-macOKMar 09 2025
R-4.4-linuxOKMar 09 2025
R-4.3-winOKMar 09 2025
R-4.3-macOKMar 09 2025

Exports:mse_saekernelsaekernel

Dependencies:

wicaksh_vignette

Rendered fromwicaksh_vignette.Rmdusingknitr::rmarkdownon Mar 09 2025.

Last update: 2021-05-31
Started: 2021-05-31