David S. Matteson

David S.
Matteson
Assistant Professor
Department of Statistical Science (CIS) and Department of Social Statistics (ILR)

David S. Matteson is Assistant Professor of Statistical Science and Social Statistics at Cornell University, where he is a member of the ILR SchoolCenter for Applied MathematicsField of Operations Research, and the Program in Financial Engineering, and teaches statistics and financial engineering courses. Professor Matteson received his PhD in Statistics at the University of Chicago (2008) and his BSB in Finance, Mathematics, and Statistics at the University of Minnesota (2003). He has received a CAREER Award from the National Science Foundation (2015), a Faculty Research Award from the Xerox/PARC Foundation (2014), and Best Paper Awards from the annual R/Finance conference (2011, 2013) and the National Association of Rehabilitation Research (2015). He is an Associate Editor of the Journal of the American Statistical Association-Theory and Methods, Biometrics, and Statistica Sinica. He is an Officer for the Business and Economic Statistics Section of the American Statistical Association, and a member of the Institute of Mathematical Statistics and the International Biometric Society. He is coauthor of Statistics and Data Analysis for Financial Engineering (2nd ed., Springer, 2015).

RESEARCH INTERESTS

I am particularly interested in high dimensional data analysis, which has become one of the most important areas of statistical research, with applications in many fields. While many recent efforts have attempted to incorporate mathematical and contextual constraints into analysis, most work on these difficult problems has focused on independent observations. I have found dependence to be a valuable source of additional information, not merely an additional complexity, and I endeavor to make major contributions to this exciting new field.

As a data scientist, my primary research focus has involved the analysis of complex big data and the development of accompanying statistical methodology. This includes data structured by time indices or spatial locations, as well as functional and network structured data. My research has been heightened by biological, environmental, financial, operational and sociological applications. My collaborations with scientists and industry professionals continue to be mutually rewarding and personally enjoyable. My research interests include:

  • Applied Biophysics
  • Bayesian Analysis
  • Biostatistics
  • Dimension Reduction
  • Disability
  • Emergency Medical Services
  • Financial Econometrics
  • Functional Data Analysis
  • Machine Learning
  • Multivariate Statistics
  • Neuroscience
  • Nonparametrics
  • Point Processes
  • Semiparametrics
  • Signal Processing
  • Spatio-Temporal Modeling
  • Sustainable Energy
  • Time Series

 

Publications

My most current preprints are here: arXiv.org.

Preprints

Risk, B., Matteson, D.S. and Ruppert, D. (2015), “Likelihood Component Analysis.”

Tupper, L., Matteson, D.S., and Anderson, C.L. (2015), “Band Depth Clustering for Nonstationary Time Series and Wind Speed Behavior.”

James, N.A. and Matteson, D.S. (2015), “Change Points via Probabilistically Pruned Objectives.”

Zhou, Z., and Matteson, D.S. (2015), “Predicting Melbourne Ambulance Demand Using Kernel Warping.”

Nicholson, W.B., Matteson, D.S. and Bien, J. (2015), “VARX-L: Structured Regularization for Large Vector Autoregressions with Exogenous Variables.”

James, N.A., Kejariwal, A. and Matteson, D.S. (2015), “Leveraging Cloud Data to Mitigate User Experience from Breaking Bad: The Twitter Approach.”

Nicholson, W.B., Bien, J. and Matteson, D.S. (2015), “HVAR: High Dimensional Forecasting via Interpretable Vector Autoregression.”

Kowal, D.R., Matteson, D.S. and Ruppert, D. (2015), “A Bayesian Multivariate Functional Dynamic Linear Model.”

Articles

Matteson, D.S. and Tsay, R.S. (2015), “Independent Component Analysis via Distance Covariance,” To Appear, Journal of the American Statistical Association.

Westgate, B.S., Woodard, D.B., Matteson, D.S. and Henderson, S.G. (2015), “Large-Network Travel Time Estimation for Ambulance Fleet Management,” To Appear, European Journal of Operational Research.

Zhou, Z., Matteson, D.S., Woodard, D.B., Micheas, A.C. and Henderson, S.G. (2015), “A Spatio-Temporal Point Process Model for Ambulance Demand,” Journal of the American Statistical Association, Vol. 110, No. 509, 6-15.

Zhou, Z., and Matteson, D.S. (2015), “Predicting Ambulance Demand: A Spatio-Temporal Kernel Approach,” Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2297-2303.

James, N.A. and Matteson, D.S. (2015), “ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data,” Journal of Statistical Software, Vol. 62, No. 7: 1-25.

Matteson, D.S. and James, N.A. (2014), “A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data,” Journal of the American Statistical Association, Vol. 109, No. 505, 334-345.

Risk, B., Matteson, D.S., Ruppert, D., Eloyan, A. and Cao, B. (2014), “An Evaluation of Independent Component Analyses with an Application to Resting State fMRI,” Biometrics, Vol. 70, No. 1: 224-236.

Erickson, W.A., von Schrader, S., Bruyre, S., VanLooy, S., and Matteson, D.S. (2014) “Disability-Inclusive Employer Practices and Hiring of Individuals with Disabilities,” Rehabilitation Research, Policy, and Education, Vol. 28, No. 4, 309-328.

Matteson, D.S., James, N.A., Nicholson, W.B. and Segalini, L.C. (2013), “Locally Stationary Vector Processes and Adaptive Multivariate Modeling,” Acoustics, Speech and Signal Processing, IEEE, 8722-8726.

Westgate, B.S., Woodard, D.B., Matteson, D.S. and Henderson, S.G. (2013), “Travel Time Estimation for Ambulances using Bayesian Data Augmentation,” Annals of Applied Statistics, Vol. 7, No. 2, 1139-1161.

Holan, S.H., Yang, W.-H., Matteson, D.S. and Wikle, C.K. (2012), “An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models,” Applied Stochastic Models in Business and Industry, Vol. 28, No. 6, 485-499.

Holan, S.H., Yang, W.-H., Matteson, D.S. and Wikle, C.K. (2012), “Rejoinder, An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models,” Applied Stochastic Models in Business and Industry, Vol. 28, No. 6, 504-505.

Matteson, D.S. and Ruppert, D. (2011), “GARCH Models of Dynamic Volatility and Correlation,” Signal Processing Magazine, IEEE, Vol. 28, No. 5, 72-82.

Woodard, D.B., Matteson, D.S. and Henderson S.G. (2011), “Stationarity of Generalized Autoregressive Moving Average Models,” Electronic Journal of Statistics, Vol. 5, No. 0, 800-828.

Matteson, D.S., McLean, M.W., Woodard, D.B. and Henderson, S.G. (2011), “Forecasting Emergency Medical Service Call Arrival Rates,” Annals of Applied Statistics, Vol. 5, No. 2B, 1379-1406.

Matteson, D.S. and Tsay, R.S. (2011), “Dynamic Orthogonal Components for Multivariate Time Series,” Journal of the American Statistical Association, Vol. 106, No. 496, 1450-1463.

Matteson, D.S. and Tsay, R.S. (2007), “High Dimensional Volatility Models,” JSM Proceedings, Business and Economics Statistics Section, Alexandria, VA: American Statistical Association, 1006-1013.

Textbook

Ruppert, D., Matteson, D.S. (2015). Statistics and Data Analysis for Financial Engineering (2nd ed., pp. 721). New York, NY: Springer.

Book Chapters

Matteson, D.S., James, N.A. and Nicholson, W.B. (2015), “Statistical Measures of Dependence For Financial Data,” In Press, Financial Signal Processing and Machine Learning, Wiley.

Zhou, Z. and Matteson, D.S. (2015), “Temporal and Spatio-Temporal Models for Ambulance Demand,” In Press, Healthcare Data Analysis, Wiley.

R Packages