Research

The list below is a (non-exhaustive) collection of papers concerned with developing or applying statistical methods for functional data analysis authored by group members.

20142011
20132010-2003
2012 

 

Under Review

Adam Ciarleglio and R. Todd Ogden. Wavelet-Based Scalar-on-Function Finite Mixture Regression Models. [Link

J. Goldsmith, V. Zipunnikov and J. Schrack. Generalized Multilevel Functional-on-Scalar Regression and Principal Component Analysis. [PDF

J. Goldsmith and T. Kitago. Assessing Systematic Effects of Stroke on Motor Control using Hierarchical Function-on-Scalar Regression. [PDF

S. Lee, V. Zipunnikov, B. S. Caffo, D. S. Reich, and D. L. Pham. Statistical Image Analysis of Longitudinal RAVENS Images: Methodology and Case Study. [PDF]

 

To Appear

Y. Zhao, H. Chen, and R. T. Ogden. Wavelet-based Adaptive LASSO and Screening approaches in functional linear regression. Journal of Computational and Graphical Statistics. [PDF

S. Lopez-Pintado, Y. Sun, J. K. Lin, M. G. Genton (Accepted). Simplicial Band depth for multivariate functional data. Advances in Data analysis and Classification. 

A. Alonso, D. Casado, S. Lopez-Pintado and J. Romo (Accepted). Functional data based methods for time series classification. Journal of Classification. 

H. Chen, P. T. Reiss, and T. Tarpey (Accepted). Optimally weighted L^2 distance for functional data.Biometrics. [Link

B. Swihart, J. Goldsmith, C. M. Crainiceanu (Accepted). Restricted Likelihood Ratio Tests for Functional Effects in the Functional Linear Model. Technometrics. [PDF

J. A. Schrack, V. Zipunnikov, J. Goldsmith, J. Bai, E. M. Simonsick, C. M. Crainiceanu, L. Ferrucci (Accepted). Assessing the "Physical Cliff": Detailed Quantification of Aging and Physical Activity. Journal of Gerontology: Medical Sciences. 
 

2014

I. W. McKeague and M. Qian (2014). Estimation of Treatment Policies Based on Functional Predictors.Statistica Sinica, 23 46-64. [Link | PubMed

J. Goldsmith, L. Huang, C. M. Crainiceanu (2014). Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection. Journal of Computational and Graphical Statistics, 23 46-64. [Link | PDF

P. T. Reiss, L. Huang, Y.-H. Chen, L. Huo, T. Tarpey, and M. Mennes (2014). Massively parallel nonparametric regression, with an application to developmental brain mapping. Journal of Computational and Graphical Statistics. 23 232-248. [Link | PubMed

J. Goldsmith, L. Huang, C. M. Crainiceanu (Accepted). Smooth Scalar-on-Image Regression via Spatial Bayesian Variable Selection. Journal of Computational and Graphical Statistics. 23 46-64. [Link | PDF

J. Goldsmith, F. Scheipl (2014). Estimator Selection and Combination in Scalar-on-Function Regression.Computational Statistics and Data Analysis. 70 362-372. [Link | PDF

J. A. Schrack, V. Zipunnikov, J. Goldsmith, K. Bandeen-Roche, C. M. Crainiceanu, L. Ferrucci (2014). Estimating Energy Expenditure from Heart Rate in Older Adults: a Case for Calibration. PLoS One, 9 1-9. [Link]

 

2013

J. Goldsmith, S. Greven, C. M. Crainiceanu (2013). Corrected Confidence Bands for Functional Data Using Principal Components. Biometrics. 69 41-51. [Link | PubMed

A. Torrente, S. Lopez-Pintado and J. Romo (2013). DepthTools: An R package for robust analysis of gene expression data. BMC Bioinformatics. 14 237. [Link | PubMed

S. Lopez-Pintado and I. McKeague (2013). Recovering gradients from sparsely observed functional data.Biometrics. 69 396-404. [Link | PubMed

S. Lee, V. Zipunnikov, B. Caffo, C. Crainiceanu, D. Reich, N. Shiee, D. Pham (2013). Clustering of High-dimensional Longitudinal Brain Images. Proceedings of 3rd biennial International Conference on Information Processing in Medical Imaging (IPMI 2013). [Link

H. Shou, A. Eloyan, S. Lee, V. Zipunnikov, M. Nebel, B. Caffo, M. Lindquist (2013). Quantifying the reliability of image replication studies: The image intra-class correlation coefficient (I2C2). Cognitive, Affective, and Behavioral Neuroscience. 13 714-724. [Link | PubMed

J. Gertheiss, J. Goldsmith, C. M. Crainiceanu, S. Greven (2013). Longitudinal Scalar-on-Functions Regression with Application to Tractography Data. Biostatistics. 14 447-461. [Link | PubMed

H. Sorensen, J. Goldsmith, L. Sangalli (2013). An Introduction with Medical Applications to Functional Data Analysis. Statistics in Medicine. 32 5222-5240. [Link | PubMed
 

2012

Y. Zhao, R. T. Ogden, and P. T. Reiss (2012). Wavelet-based LASSO in functional linear regression. Journal of Computational and Graphical Statistics. 21 600-617. [Link | PubMed

J. Goldsmith, C. M. Crainiceanu, B. S. Caffo, D. S. Reich (2012). Longitudinal Penalized Functional Regression for Cognitive Outcomes on Neuronal Tract Measurements. Journal of the Royal Statistical Society: Series C.61 453-469. [Link | PubMed

T. Shinohara, J. Goldsmith, F. Mateen, D. S. Reich, C. M. Crainiceanu (2012). Predicting Breakdown of the Blood-Brain Barrier in Multiple Sclerosis Without Contrast Agents. American Journal of Neuroradiology. 331586-1590. [Link | PubMed

J. Bai, J. Goldsmith, B. S. Caffo, T. Glass, C. M. Crainiceanu (2012). Movelets: A Dictionary of Movement.Electronic Journal of Statistics. 6 559-578. [Link | PubMed
 

2011

S. Lee, H. Shen, Y. Truong, M. Lewis, X. Huang (2011). Independent Component Analysis Involving Autocorrelated Sources with an Application to Functional Magnetic Resonance Imaging. Journal of the American Statistical Association. 106 1009-1024. [Link

J. Goldsmith, B. S. Caffo, C. M. Crainiceanu, D. Reich, Y. Du, C. W. Hendrix (2011). Nonlinear Tube-Fitting for the Analysis of Anatomical and Functional Structures. Annals of Applied Statistics. 5 337-363. [Link | PubMed

J. Goldsmith, J. Bobb, C. M. Crainiceanu, B. S. Caffo, D. S. Reich (2011). Penalized Functional Regression.Journal of Computational and Graphical Statistics. 20 830-851. [Link | PubMed

J. Goldsmith, C. M. Crainiceanu, B. S. Caffo, D. S. Reich (2011). Penalized Functional Regression Analysis of White-Matter Tract Profiles in Multiple Sclerosis. NeuroImage. 57 431-439. [Link | PubMed

J. Goldsmith, M. P. Wand, and C. M. Crainiceanu (2011). Functional Regression via Variational Bayes.Electronic Journal of Statistics. 5 572-602. [Link | PubMed
 

2010 and before

S. Lopez-Pintado and J. Romo (2010). A half-region depth for functional data. Computational Statistics and Data Analysis. 55 1679-1695. [Link

S. Lopez-Pintado, J. Romo and A. Torrente (2010). Robust depth-based tools for the analysis of gene expression data. Biostatistics. 11 254-264. [Link | PubMed

R. T. Ogden and E. Greene. (2010). Wavelet modeling of functional random effects with application to human vision data. Journal of Statistical Planning and Inference. 140 3797-3808. [Link

R. T. Ogden and H. Jiang (2010). Nonparametric evaluation of heterogeneity of brain regions in neuroreceptor mapping applications. Statistics and Its Interface. 3 59-67. [Link

P. T. Reiss and R. T. Ogden (2010). Functional generalized linear models with images as predictors.Biometrics. 66 61-69. [Link | PubMed

C. M. Crainiceanu, J. Goldsmith (2010). Bayesian Functional Data Analysis using WinBUGS. Journal of Statistical Software. 32 1-33. [Link | PubMed

C. Chang and R. T. Ogden (2009). Bootstrapping sums of independent but not identically distributed continuous processes with applications to functional data. Journal of Multivariate Analysis. 100 1291-1303. [Link

P. T. Reiss and R. T. Ogden (2009). Smoothing parameter selection for a class of semiparametric linear models. Journal of the Royal Statistical Society, Series B. 71 505-523. [Link

S. Lopez-Pintado and J. Romo (2009). On the concept of depth for functional data. Journal of the American Statistical Association. 104 486-503. [Link

P. T. Reiss and R. T. Ogden (2007). Functional principal component regression and functional partial least squares. Journal of the American Statistical Association. 102 984-996. [Link

S. Lopez-Pintado and J. Romo (2007). Depth-based inference for functional data. Computational Statistics and Data Analysis. 51 4957-4968. [Link

S. Lopez-Pintado and R. Jornsten (2007). Functional Analysis via extensions of the band depth. IMS Lecture Notes-Monograph Series. 54 103-119. [PDF

T. Tarpey, E. Petkova, and R. T. Ogden (2003). Profiling placebo responders by self-consistent partitioning of functional data. Journal of the American Statistical Association. 98 850-858. [Link

R. T. Ogden, C. E. Miller, K. Takezawa, and S. Ninomiya (2002). Functional regression in crop lodging assessment with digital images. Journal of Agricultural, Biological and Environmental Statistics. 7 389-402. [Link