Advanced Statistical Methods and Models

This paper introduces a fast generalized LFPCA method for analyzing longitudinal functional data, showing it is faster and scalable while applied to neuronal firing rate data under DBS.

This study introduces a robust cooperative learning method for multiview regression that reduces outlier influence while preserving view information sharing, showing improved predictive performance in labor onset prediction.

The paper introduces an unbiased median estimator for even sample sizes, showing it is more accurate and has lower variance than the conventional median in asymmetric distributions.

The paper identifies influential data points in linear regression analyses of 100 scientific papers, finding that 29 relied on a single influential point for their conclusions.

The paper introduces a functional nonparametric method to compare covariance structures in dementia studies, applied to ADNI data to analyze biomarker differences.

The paper introduces a method to reduce outlier influence in EEG data using principal component projection weights in linear models, improving statistical power and controlling error rates.

This paper introduces AdaReg, a data-adaptive robust estimation method for linear regression, showing improved performance on GTEx gene expression data compared to fixed gamma methods.

The paper provides guidelines for using modern statistical methods in neuroscience to improve power and control Type I error rates.