Publications

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.”

Articles

Kowal, D.R., Matteson, D.S. and Ruppert, D. (2015), “A Bayesian Multivariate Functional Dynamic Linear Model,” To Appear, Journal of the American Statistical Association.

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.

Home