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Variable Selection in GLM and Cox Models with Second-Generation P-Values
Yi Zuo, Thomas G. Stewart, Jeffrey D. Blume
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-Val…
Variable Selection with Second-Generation P-Values
Yi Zuo, Thomas G. Stewart, Jeffrey D. Blume
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 sele…
False Discovery Rate Computation: Illustrations and Modifications
Megan Hollister Murray, Jeffrey D. Blume
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 o…
Likelihood Based Study Designs for Time-to-Event Endpoints
Jeffrey D Blume, Leena Choi
Likelihood methods for measuring statistical evidence obey the likelihood principle while maintaining bounded and well-controlled frequency properties. These methods lend themselve…
Bagged Empirical Null p-values: A Method to Account for Model Uncertainty in Large Scale Inference
Sarah Fletcher Mercaldo, Jeffrey D. Blume
When conducting large scale inference, such as genome-wide association studies or image analysis, nominal -values are often adjusted to improve control over the family-wise erro…