J
Joseph P. Romano
Researcher at Stanford University
Publications - 140
Citations - 12716
Joseph P. Romano is an academic researcher from Stanford University. The author has contributed to research in topics: Multiple comparisons problem & Estimator. The author has an hindex of 50, co-authored 139 publications receiving 11484 citations. Previous affiliations of Joseph P. Romano include University of California, Berkeley & University of California, San Diego.
Papers
More filters
Journal ArticleDOI
The stationary bootstrap
TL;DR: In this paper, the stationary bootstrap technique was introduced to calculate standard errors of estimators and construct confidence regions for parameters based on weakly dependent stationary observations, where m is fixed.
Journal ArticleDOI
Large Sample Confidence Regions Based on Subsamples under Minimal Assumptions
TL;DR: In this paper, Wu et al. studied the problem of constructing confidence regions by approximating the sampling distribution of some statistic, where the true sampling distribution is estimated by an appropriate normalization of the values of the statistic computed over subsamples of the data.
Journal ArticleDOI
Stepwise Multiple Testing as Formalized Data Snooping
Michael Wolf,Joseph P. Romano +1 more
TL;DR: In this paper, a stepwise multiple testing procedure is proposed to asymptotically control the familywise error rate at a desired level, which implicitly captures the joint dependence structure of the test statistics, which results in increased ability to detect alternative hypotheses.
Book
Stepwise multiple testing as formalized data snooping
Joseph P. Romano,Michael Wolf +1 more
TL;DR: In this article, a stepwise multiple testing procedure that asymptotically controls the familywise error rate is proposed, which implicitly captures the joint dependence structure of the test statistics, which results in increased ability to detect false hypotheses.
Journal ArticleDOI
Empirical Likelihood is Bartlett-Correctable
TL;DR: In this article, it was shown that the empirical likelihood method for constructing confidence intervals is Bartlett-correctable, which means that a simple adjustment for the expected value of log-likelihood ratio reduces coverage error to an extremely low O(n −2 ) where n −2 denotes sample size.