Zobrazeno 1 - 10
of 222
pro vyhledávání: '"Blume, Jeffrey D"'
Many adaptive monitoring schemes adjust the required evidence toward a hypothesis to control Type I error. This shifts focus away from determining scientific relevance with an uncompromised degree of evidence. We propose sequentially monitoring the S
Externí odkaz:
http://arxiv.org/abs/2204.10678
Variable selection has become a pivotal choice in data analyses that impacts subsequent inference and prediction. In linear models, variable selection using Second-Generation P-Values (SGPV) has been shown to be as good as any other algorithm availab
Externí odkaz:
http://arxiv.org/abs/2109.09851
Many statistical methods have been proposed for variable selection in the past century, but few balance inference and prediction tasks well. Here we report on a novel variable selection approach called Penalized regression with Second-Generation P-Va
Externí odkaz:
http://arxiv.org/abs/2012.07941
False discovery rates (FDR) are an essential component of statistical inference, representing the propensity for an observed result to be mistaken. FDR estimates should accompany observed results to help the user contextualize the relevance and poten
Externí odkaz:
http://arxiv.org/abs/2010.04680
Autor:
Godfrey, Caroline M. a, Shipe, Maren E. a, Welty, Valerie F. a, Maiga, Amelia W. a, b, Aldrich, Melinda C. c, Montgomery, Chandler c, Crockett, Jerod a, Vaszar, Laszlo T. d, Regis, Shawn e, Isbell, James M. f, Rickman, Otis B. g, Pinkerman, Rhonda b, Lambright, Eric S. a, Nesbitt, Jonathan C. a, b, Maldonado, Fabien g, Blume, Jeffrey D. h, Deppen, Stephen A. a, Grogan, Eric L. a, b, ∗
Publikováno v:
In Chest November 2023 164(5):1305-1314
Autor:
Richmond, Jennifer a, Murray, Megan Hollister e, Milder, Cato M. b, Blume, Jeffrey D. f, Aldrich, Melinda C. a, c, d, ∗
Publikováno v:
In Chest May 2023 163(5):1314-1327
Autor:
Blume, Jeffrey D, Choi, Leena
Likelihood methods for measuring statistical evidence obey the likelihood principle while maintaining bounded and well-controlled frequency properties. These methods lend themselves to sequential study designs because they measure the strength of sta
Externí odkaz:
http://arxiv.org/abs/1711.01527
Verifying that a statistically significant result is scientifically meaningful is not only good scientific practice, it is a natural way to control the Type I error rate. Here we introduce a novel extension of the p-value - a second-generation p-valu
Externí odkaz:
http://arxiv.org/abs/1709.09333
When conducting large scale inference, such as genome-wide association studies or image analysis, nominal $p$-values are often adjusted to improve control over the family-wise error rate (FWER). When the majority of tests are null, procedures control
Externí odkaz:
http://arxiv.org/abs/1707.05833
Missing data are a common problem for both the construction and implementation of a prediction algorithm. Pattern mixture kernel submodels (PMKS) - a series of submodels for every missing data pattern that are fit using only data from that pattern -
Externí odkaz:
http://arxiv.org/abs/1704.08192