77 citations · 81 across the 8 of their papers we have counts for
5 papers · 1 filter
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…
An Empirical Bayes Approach to Regularization Using Previously Published Models
Derek K Smith, Loren E Smith, Brett Kroncke +3
This manuscript proposes a novel empirical Bayes technique for regularizing regression coefficients in predictive models. When predictions from a previously published model are ava…
Second-generation p-values: improved rigor, reproducibility, & transparency in statistical analyses
Jeffrey D. Blume, Lucy DAgostino McGowan, William D. Dupont +1
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 i…
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…
Missing Data and Prediction
Sarah Fletcher Mercaldo, Jeffrey D. Blume
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…