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

Calculates the optimal level of significance 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, Jae H. and Choi, In, Choosing the Level of Significance: A Decision-Theoretic Approach (December 18, 2017), available at SSRN: <https://ssrn.com/abstract=2652773> or <doi:10.2139/ssrn.2652773>. See also Kim and Ji (2015) <doi:10.1016/j.jempfin.2015.08.006>.

Package details

AuthorJae H. Kim <[email protected]>
MaintainerJae H. Kim <[email protected]>
Package repositoryView on CRAN
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OptSig documentation built on Dec. 23, 2017, 5:34 p.m.