The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance not pay attention to the accuracy of prediction, but focus on model selection uncertainty and providing more information of the model selection results. And to estimate the model measures, we propose an simplify and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. We propose a new procedure, called model uncertainty variance, which can quantify the uncertainty of model selection on Autoregressive Moving Average models. The model uncertainty variance focuses on model selection uncertainty and providing more information of the model selection results. To estimate the model uncertainty variance, we propose an simplified and faster algorithm based on bootstrap method, which is proven to be effective and feasible by Monte-Carlo simulation. At the same time, we also made some optimizations and adjustments to the Model Confidence Bounds algorithm, so that it can be applied to the time series model selection method. The consistency of the algorithm result is also verified by Monte-Carlo simulation. Please see Li,Y., Luo,Y., Ferrari,D., Hu,X. and Qin,Y. (2019) Model Confidence Bounds for Variable Selection. Biometrics, 75:392-403.<doi:10.1111/biom.13024> for more information.
| Version: | 0.1.6 |
| Imports: | boot, forecast, ggplot2, hash |
| Published: | 2023-01-09 |
| DOI: | 10.32614/CRAN.package.uotm |
| Author: | Heming Deng Developer [aut, cre, cph], Sunan Gao Dev [aut] |
| Maintainer: | Heming Deng Developer <dheming at ruc.edu.cn> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| CRAN checks: | uotm results |
| Reference manual: | uotm.pdf |
| Package source: | uotm_0.1.6.tar.gz |
| Windows binaries: | r-devel: uotm_0.1.6.zip, r-release: uotm_0.1.6.zip, r-oldrel: uotm_0.1.6.zip |
| macOS binaries: | r-release (arm64): uotm_0.1.6.tgz, r-oldrel (arm64): uotm_0.1.6.tgz, r-release (x86_64): uotm_0.1.6.tgz, r-oldrel (x86_64): uotm_0.1.6.tgz |
Please use the canonical form https://CRAN.R-project.org/package=uotm to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.