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.
- Version0.1.1
- R versionunknown
- LicenseGPL-3
- Needs compilation?No
- Last release06/04/2021
Documentation
Team
Wicak Surya Hasani
Azka Ubaidillah
Show author detailsRolesAuthor
Insights
Last 30 days
This package has been downloaded 155 times in the last 30 days. More than a random curiosity, but not quite a blockbuster. Still, it's gaining traction! The following heatmap shows the distribution of downloads per day. Yesterday, it was downloaded 2 times.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Last 365 days
This package has been downloaded 2,202 times in the last 365 days. Consider this 'mid-tier influencer' status—if it were a TikTok, it would get a nod from nieces and nephews. The day with the most downloads was Sep 11, 2024 with 25 downloads.
The following line graph shows the downloads per day. You can hover over the graph to see the exact number of downloads per day.
Data provided by CRAN
Binaries
Dependencies
- Suggests3 packages