The R Journal: article published in 2019, volume 11:1

ciuupi: An R package for Computing Confidence Intervals that Utilize Uncertain Prior Information PDF download
Rheanna Mainzer and Paul Kabaila , The R Journal (2019) 11:1

Abstract We have created the R package ciuupi to compute confidence intervals that utilize uncertain prior information in linear regression. Unlike post-model-selection confidence intervals, the confidence interval that utilizes uncertain prior information (CIUUPI) implemented in this package has, to an excellent approximation, coverage probability throughout the parameter space that is very close to the desired minimum coverage probability. Furthermore, when the uncertain prior information is correct, the CIUUPI is, on average, shorter than the standard confidence interval constructed using the full linear regression model. In this paper we provide motivating examples of scenarios where the CIUUPI may be used. We then give a detailed description of this interval and the numerical constrained optimization method implemented in R to obtain it. Lastly, using a real data set as an illustrative example, we show how to use the functions in ciuupi.

Received: 2018-05-01; online 2019-08-17, supplementary material, (1 Kb)
CRAN packages: ciuupi, nloptr, statmod
CRAN Task Views implied by cited CRAN packages: Distributions, NumericalMathematics, Optimization


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This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

@article{RJ-2019-026,
  author = {Rheanna Mainzer and Paul Kabaila},
  title = {{ciuupi: An R package for Computing Confidence Intervals that
          Utilize Uncertain Prior Information}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-026},
  url = {https://doi.org/10.32614/RJ-2019-026}
}