Nathaniel E Helwig

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Contact Me

Department of Psychology
N218 Elliott Hall

75 E River Rd

Educational Background & Specialties

Educational Background

  • PhD: Quantitative Psychology, University of Illinois, Urbana-Champaign, 2013.
  • MA: Psychology, University of Illinois, Urbana-Champaign, 2011.
  • MS: Statistics, University of Illinois, Urbana-Champaign, 2010.
  • BS: Psychology & Mathematics, University of Miami, Coral Gables, FL, 2007.

Curriculum Vitae

  • For a full list of publications, see
  • Kage, C. C., Akbari-Shandiz, M., Foltz, M. H., Lawrence, R. L., Brandon, T. L., Helwig, N. E., & Elllingson, A. M. (2020). Validation of an automated shape-matching algorithm for biplane radiographic spine osteokinematics and radiostereometric analysis error quantification. PLoS ONE, 15(2), e0228594.
  • Almquist, Z. W., Helwig, N. E., & You, Y. (2020). Connecting Continuum of Care point-in-time homeless counts to United States Census areal units. Mathematical Population Studies, 27(1), 46-58.
  • Helwig, N. E. (2020). Multiple and generalized nonparametric regression. In P. Atkinson, S. Delamont, A. Cernat, J. W. Sakshaug, & R. A. Williams (Eds.), SAGE Research Methods Foundations.
  • Hammell, A. E., Helwig, N. E., Kaczkurkin, A. N., Sponheim, S. R., & Lissek, S. (2020). The temporal course of over-generalized conditioned threat expectancies in posttraumatic stress disorder. Behaviour Research and Therapy, 124, 103513.
  • Helwig, N. E. (2019). Robust nonparametric tests of general linear model coefficients: A comparison of permutation methods and test statistics. NeuroImage, 201, 116030.
  • Helwig, N. E., & Snodgress, M. A. (2019). Exploring individual and group differences in latent brain networks using cross-validated simultaneous component analysis. NeuroImage, 201, 116019.
  • Helwig, N. E. (2019). Statistical nonparametric mapping: Multivariate permutation tests for location, correlation, and regression problems in neuroimaging. WIREs Computational Statistics, 11(2), e1457.
  • Whiteford, K. L., Schloss, K. B., Helwig, N. E., & Palmer, S. E. (2018). Color, music, and emotion: Bach to the blues. i-Perception, 9(5), 1-25.
  • Lyford-Pike, S., Helwig, N. E., Sohre, N. E., Guy, S. J., & Hadlock, T. A. (2018). Predicting perceived disfigurement from facial function in patients with unilateral paralysis. Plastic and Reconstructive Surgery, 142(5), 722e-728e.
  • Helwig, N. E., & Ruprecht, M. R. (2017). Age, gender, and self-esteem: a sociocultural look through a nonparametric lens. Archives of Scientific Psychology, 5(1), 19-31.
  • Helwig, N. E. (2017). Regression with ordered predictors via ordinal smoothing splines. Frontiers in Applied Mathematics and Statistics, 3(15), 1-13.
  • Helwig, N. E. (2017). Estimating latent trends in multivariate longitudinal data via Parafac2 with functional and structural constraints. Biometrical Journal, 59(4), 783-803.
  • Helwig, N. E., Sohre, N. E., Ruprecht, M. R., Guy, S. J., & Lyford-Pike, S. (2017). Dynamic properties of successful smiles. PLoS ONE, 12(6), e0179708.
  • Helwig, N. E. (2017). Adding bias to reduce variance in psychological results: A tutorial on penalized regression. The Quantitative Methods for Psychology, 13(1), 1-19.
  • Helwig, N. E., Shorter, K. A., Ma, P., & Hsiao-Wecksler, E. T. (2016). Smoothing spline analysis of variance models: A new tool for the analysis of cyclic biomechanical data. Journal of Biomechanics, 49(14), 3216-3222.
  • Helwig, N. E., & Ma, P. (2016). Smoothing spline ANOVA for super-large samples: Scalable computation via rounding parameters. Statistics and Its Interface, 9(4), 433-444.
  • Helwig, N. E. (2016). Efficient estimation of variance components in nonparametric mixed-effects models with large samples. Statistics and Computing, 26(6), 1319-1336.
  • Helwig, N. E., & 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(3), 715-732.
  • Helwig, N. E. (2013). The special sign indeterminacy of the direct-fitting Parafac2 model: Some implications, cautions, and recommendations for Simultaneous Component Analysis. Psychometrika, 78(4), 725-739.
  • Helwig, N. E., & Hong, S. (2013). A critique of Tensor Probabilistic Independent Component Analysis: Implications and recommendations for multi-subject fMRI data analysis. Journal of Neuroscience Methods, 213(2), 263-273.
  • Helwig, N. E., Hong, S., & Polk, J. D. (2012). Parallel Factor Analysis of gait waveform data: A multimode extension of Principal Component Analysis. Human Movement Science, 31(3), 630-648.
  • Helwig, N. E., Hong, S., Hsiao-Wecksler, E. T., & Polk, J. D. (2011). Methods to temporally align gait cycle data. Journal of Biomechanics, 44(3), 561-566.
  • Distinguished Dissertation Award, American Psychological Association (Division 5), Spring 2014
  • Student Paper Competition Winner, American Statistical Association (Statistical Computing Section), Spring 2013
  • Nancy Hirschberg Award (for outstanding original research or scholarship), Department of Psychology, University of Illinois, Spring 2013
  • Woodrow Fellowship (for outstanding original research or scholarship), Department of Psychology, University of Illinois, Spring 2011
  • Outstanding Quantitative Psychology Doctoral Student, Department of Psychology, University of Illinois, Spring 2010
  • Graduate Teaching Certificate, Center for Teaching Excellence, University of Illinois, Spring 2010
  • Outstanding Teacher, Incomplete List of Teachers ranked as Excellent, University of Illinois, Fall 2009 - Spring 2010