OptSig: Optimal Level of Significance for Regression and Other Statistical Tests

The optimal level of significance is calculated based on a decision-theoretic approach. The optimal level is chosen so that the expected loss from hypothesis testing is minimized. A range of statistical tests are covered, including the test for the population mean, population proportion, and a linear restriction in a multiple regression model. The details are covered in Kim and Choi (2020) <doi:10.1111/abac.12172>, and Kim (2021) <doi:10.1080/00031305.2020.1750484>.

Getting started

Package details

AuthorJae H. Kim <jaekim8080@gmail.com>
MaintainerJae H. Kim <jaekim8080@gmail.com>
Package repositoryView on CRAN
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OptSig documentation built on July 3, 2022, 5:05 p.m.