Neuroimaing approaches such as electroencephalograph (EEG) and functional magnetic resonance imaging (fMRI) record massive amounts of data, and we need reliable methods for analyzing these data. My research focuses on the development of computationally scalable nonparametric and multivariate methods for analyzing neuroimaging data. This work is implemented in my R packages eegkit, ica, multiway, npreg, and nptest.
- Robust nonparametric tests of general linear model coefficients: A comparison of permutation methods and test statistics. NeuroImage (2019).
- Exploring individual and group differences in latent brain networks using cross-validated simultaneous component analysis. NeuroImage (2019).
- Statistical nonparametric mapping: Multivariate permutation tests for location, correlation, and regression problems in neuroimaging. WIREs Computational Statistics (2019).
- Smoothing spline ANOVA for super-large samples: Scalable computation via rounding parameters. Statistics and Its Interface (2016).
- Efficient estimation of variance components in nonparametric mixed-effects models with large samples. Statistics and Computing (2016).
- Bootstrap enhanced penalized regression for variable selection with neuroimaging data. Frontiers in Neuroscience (2016).
- A critique of Tensor Probabilistic Independent Component Analysis: Implications and recommendations for multi-subject fMRI data analysis. Journal of Neuroscience Methods (2013).