Nathaniel E. Helwig, Ph.D.

Affiliations: 
2013 Psychology University of Illinois, Urbana-Champaign, Urbana-Champaign, IL 
Area:
Psychometrics Psychology, Statistics, Computer Science
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"Nathaniel Helwig"

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Ping Ma grad student 2013 UIUC
 (Fast and stable smoothing spline analysis of variance models for large samples with applications to electroencephalography data analysis.)
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Publications

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Helwig NE. (2020) Spectrally Sparse Nonparametric Regression via Elastic Net Regularized Smoothers Journal of Computational and Graphical Statistics. 1-22
Helwig NE, Snodgress MA. (2019) Exploring individual and group differences in latent brain networks using cross-validated simultaneous component analysis. Neuroimage. 201: 116019
Helwig NE. (2016) Estimating latent trends in multivariate longitudinal data via Parafac2 with functional and structural constraints. Biometrical Journal. Biometrische Zeitschrift
Helwig NE, Shorter KA, Ma P, et al. (2016) Smoothing spline analysis of variance models: A new tool for the analysis of cyclic biomechanical data. Journal of Biomechanics
Abram SV, Helwig NE, Moodie CA, et al. (2016) Bootstrap Enhanced Penalized Regression for Variable Selection with Neuroimaging Data. Frontiers in Neuroscience. 10: 344
Helwig NE, Ma P. (2016) Smoothing spline ANOVA for super-large samples: scalable computation via rounding parameters Statistics and Its Interface. 9: 433-444
Helwig NE, Ma P. (2015) Fast and Stable Multiple Smoothing Parameter Selection in Smoothing Spline Analysis of Variance Models With Large Samples Journal of Computational and Graphical Statistics. 24: 715-732
Helwig NE, Gao Y, Wang S, et al. (2015) Analyzing spatiotemporal trends in social media data via smoothing spline analysis of variance Spatial Statistics. 14: 491-504
Helwig NE. (2015) Efficient estimation of variance components in nonparametric mixed-effects models with large samples Statistics and Computing
Helwig NE. (2013) The special sign indeterminacy of the direct-fitting Parafac2 model: some implications, cautions, and recommendations for simultaneous component analysis. Psychometrika. 78: 725-39
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