Smoothing Splines
Smoothing spline regression models offer a powerful nonparametric framework for applied research, but can be computationally impractical---or infeasible---when analyzing Big Data. My research focuses on algorithms and approximations for fitting smoothing spline regression models to ultra large samples of data, as well as tools for practical use of smoothing splines in applied research. This work is implemented in my R packages bigsplines and npreg.
Methodology
- Multiple and generalized nonparametric regression. SAGE Research Methods Foundations (2020).
- Regression with ordered predictors via ordinal smoothing splines. Frontiers in Applied Mathematics and Statistics (2017).
- Efficient estimation of variance components in nonparametric mixed-effects models with large samples. Statistics and Computing (2016).
- Smoothing spline ANOVA for super-large samples: Scalable computation via rounding parameters. Statistics and Its Interface (2016).
- Fast and stable multiple smoothing parameter selection in smoothing spline analysis of variance models with large samples. Journal of Computational and Graphical Statistics (2015).
Applications
- Connecting Continuum of Care point-in-time homeless counts to United States Census areal units. Mathematical Population Studies (2020).
- The temporal course of over-generalized conditioned threat expectancies in posttraumatic stress disorder. Behaviour Research and Therapy (2020).
- The effect of glenohumeral plane of elevation on supraspinatus subacromial proximity. Journal of Biomechanics (2018).
- Age, gender, and self-esteem: a sociocultural look through a nonparametric lens. Archives of Scientific Psychology (2017).
- Dynamic properties of successful smiles. PLoS ONE (2017).
- Smoothing spline analysis of variance models: a new tool for the analysis of cyclic biomechanical data. Journal of Biomechanics (2016).
- Analyzing spatiotemporal trends in social media data via smoothing spline analysis of variance. Spatial Statistics (2015).