Welcome to my site. I am an Associate Professor in the Departments of Child &
Adolescent Psychiatry and Population Health, New York University School of Medicine, and
a Research Scientist at the Nathan S. Kline Institute for Psychiatric Research. I
received my PhD in Biostatistics from Columbia University in 2006. The following sources
of support for my research are gratefully acknowledged: National Institute of Mental
Health grant 1 F31 MH73379-01 A1 (Jun. 16, 2005-July 31, 2006); National Science
Foundation grant DMS-0907017 (Aug. 1, 2009-July 31, 2012); National Institute of
Biomedical Imaging and Bioengineering grant 1R01EB009744-01A (PI Ogden) (July 1,
2010-March 1, 2013); National Institute of Mental Health grant 1R01MH095836-01A1 (Aug. 1,
2012-Apr. 30, 2017). My primary department at NYU is also known as the Child Study
Center; for further information about me, please see my page on the Child Study Center
website, at http://www.aboutourkids.org/about_us/staff/research_faculty/philip_reiss 

NEWS: Our group at NYU is currently seeking a Master's-level research assistant.
Please contact me for further details. 

Published and in-press articles

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Massively Parallel Nonparametric Regression, with an Application to Developmental Brain Mapping (with Lei Huang, Yin-Hsiu Chen, Lan Huo, Thaddeus Tarpey, and Maarten Mennes), Journal of Computational and Graphical Statistics (2014)

We propose a penalized spline approach to performing large numbers of parallel nonparametric analyses of...

 

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Optimally Weighted L^2 Distance for Functional Data (with Huaihou Chen and Thaddeus Tarpey), Biometrics (2014)

Many techniques of functional data analysis require choosing a measure of distance between functions, with...

 

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Paradoxical Results of Adaptive False Discovery Rate Procedures in Neuroimaging Studies (with Armin Schwartzman, Feihan Lu, Lei Huang, and Erika Proal), NeuroImage (2012)

Adaptive false discovery rate (FDR) procedures, which offer greater power than the original FDR procedure...

 

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Smoothness Selection for Penalized Quantile Regression Splines (with Lei Huang), International Journal of Biostatistics (2012)

Modern data-rich analyses may call for fitting a large number of nonparametric quantile regressions. For...

 

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Resampling-Based Information Criteria for Best-Subset Regression (with Lei Huang, Joseph E. Cavanaugh, and Amy Krain Roy), Annals of the Institute of Statistical Mathematics (2012)

When a linear model is chosen by searching for the best subset among a set...

 

Preprints

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Varying-Smoother Models for Functional Responses (with Lei Huang, Huaihou Chen, and Stan Colcombe) (2013)

This paper studies estimation of a smooth function f(x,v) when we are given functional responses...

 

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Wavelet-Domain Regression with Image Predictors, and a Surprising (Non-)Result in Psychiatric Neuroimaging (with Lan Huo, R. Todd Ogden, Yihong Zhao, and Clare Kelly) (2013)

We present a set of procedures for fitting generalized linear models with scalar responses and...

 

Software

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refund: Regression with Functional Data (with Ciprian M. Crainiceanu, Jeff Goldsmith, Lei Huang, Lan Huo, and Fabian Scheipl) (2013)
 

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vows: Voxelwise Semiparametrics (with Yin-Hsiu Chen, Lei Huang, and Lan Huo) (2013)
 

Presentations

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Function-on-Scalar Regression with the refund Package, Joint Statistical Meetings (2012)
 

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Semiparametric Methods for Mapping Brain Development (with Yin-Hsiu Chen and Lan Huo), Thomas R. Ten Have Symposium on Statistics in Psychiatry (2012)
 

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Introducing Functional Data Analysis to Neuroimaging, and Vice Versa, Cornell University Statistics Seminar (2011)
 

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Massively Parallel Nonparametrics [HDS 2011 slides] (with Lei Huang), High-Dimensional Statistics: Advances and Challenges (2011)
 

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Flexible Dependence of Functional Responses on Scalar Predictors (with Lei Huang), International Biometric Society Eastern North America Region Spring Meeting (2011)
 

Blogging and press coverage

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my blog, Times of Israel (2014)
 

Dissertation

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Regression with Signals and Images as Predictors (2006)

Signal regression and image regression, in which the outcomes are scalars and the predictors are...