Repeated significance tests based on likelihood ratio statistics
Ying So, Pranab Kumar Sen
Abstract
Ying So, Pranab Kumar Sen
Abstract
For a specified decision rule, a general class of likelihood ratio based repeated significance tests is considered. An invariance principle for the likelihood ratio statistics is established and incorporated in the study of the asymptotic theory of the proposed tests. For comparing these tests with the conventional likelihood ratio tests, based solely on the target sample size, some Bahadur efficiency results are presented. The theory is then adapted in the study of some multiple comparison procedures
OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.
A contribution statement is not available in the OpenAlex record.
Method details are not available in the OpenAlex metadata.
Findings are not separately available in the OpenAlex metadata.
Limitations are not available in the OpenAlex metadata.
Application details are not available in the OpenAlex metadata.
For a specified decision rule, a general class of likelihood ratio based repeated significance tests is considered. An invariance principle for the likelihood ratio statistics is established and incorporated in the study of the asymptotic theory of the proposed tests. For comparing these tests with the conventional likelihood ratio tests, based solely on the target sample size, some Bahadur efficiency results are presented. The theory is then adapted in the study of some multiple comparison procedures
Key concepts: Likelihood-ratio test, Likelihood principle, Statistics, Mathematics, Score test, Statistical hypothesis testing, Sample size determination, Ratio test