David Matteson

People/Faculty
Professor
Social Statistics
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Contact

129 Garden Ave
1196 Comstock Hall
Ithaca, NY 14853
United States

Tel
+1 (607) 255-6231
Websites
Research Page
Overview

My primary research focus has involved the analysis of complex multivariate data and the development of accompanying statistical methodology. My research includes biological, environmental, financial, operational and sociological applications.

Areas of Expertise

Disability
Econometrics
Statistical Theory, Methods, Analysis

Publications

Journal Articles

  • . . Developing and Evaluating Deep Neural Network-Based Denoising for Nanoparticle TEM Images with Ultra-Low Signal-to-Noise..
  • . . Predicting poverty and malnutrition for targeting, mapping, monitoring, and early warning.
  • . . Probabilistic Transformer for Time Series Analysis.
  • . . Critical Risk Indicators (CRIs) for the electric power grid: A survey and discussion of interconnected effects.
  • . . Graph-Based Continual Learning.
  • . . Multivariate random forest prediction of poverty and malnutrition prevalence.
  • . . Sparse Identification and Estimation of HighDimensional Vector AutoRegressive Moving Averages.
  • . . .
  • , , , , & . . Thyroid hormone replacement therapy patterns in pregnant women and perinatal outcomes in the offspring.
  • , , & . . Dynamic Shrinkage Processes. Journal of the Royal Statistical Society: Series B, 81(4), 781-804.
  • , , & . . Cell Line Classification Using Electric Cell-substrate Impedance Sensing (ECIS). International Journal of Biostatistics, 16(1), 1-12.
  • , , & . . Linear Non-Gaussian Component Analysis via Maximum Likelihood. Journal of the American Statistical Association, 114(525), 332-343.
  • , , & . . Functional Autoregression for Sparsely Sampled Data. Journal of Business and Economic Statistics, 37(1), 97-109.
  • , , & . . ABACUS: Unsupervised Multivariate Change Detection via Bayesian Source Separation.. SIAM International Conference on Data Mining (SDM19), 603-611.
  • , , & . . Optimization and Testing in Linear Non-Gaussian Component Analysis. Statistical Analysis and Data Mining, 12(3), 141-156.
  • , , , & . . Dynamic Poverty Prediction with Vegetation Index.
  • , , , & . . Maternal Use of Thyroid Hormone Replacement Therapy Before, During and After Pregnancy: Agreement Between Self-report and Prescription Records and Group-Based Trajectory Modeling of Prescription Patterns.
  • , & . . Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statistics. Journal of Multivariate Analysis, 168, 304-322.
  • , , & . . Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence. Uncertainty in Artificial Intelligence (UAI 2018).
  • , , & . . Band Depth Clustering for Nonstationary Time Series and Wind Speed Behavior. Technometrics, 60(2), 97-109.
  • , , & . . A Bayesian Multivariate Functional Dynamic Linear Model. Journal of the American Statistical Association, 112(518), 733-744.
  • , & . . Independent Component Analysis via Distance Covariance. Journal of the American Statistical Association, 112(518), 623-637.
  • , , & . . VARX-L: Structured Regularization for Large Vector Autoregressions with Exogenous Variables. International Journal of Forecasting, 33(3), 627-651.
  • , , & . . Pruning and Nonparametric Multiple Change Point Detection. IEEE ICDM 12th International Workshop on Spatial and Spatiotemporal Data Mining, 288-295.
  • , , , & . . Large-Network Travel Time Distribution Estimation, with Application to Ambulance Fleet Management. European Journal of Operational Research, 252(1), 322-333.
  • , , , & . . Spatiotemporal Mixed Modeling of Multi-subject fMRI via Method of Moments. Neuroimage, 142, 280-292.
  • , , & . . Mixed Data and Classification of Transit Stops. 2016 IEEE International Conference on Big Data (Big Data), 2225-2232.
  • , & . . Predicting Melbourne Ambulance Demand Using Kernel Warping. Annals of Applied Statistics, 10(4), 1977-1996.
  • , , & . . Leveraging Cloud Data to Mitigate User Experience from Breaking Bad: The Twitter Approach. 2016 IEEE International Conference on Big Data (Big Data), 3499-3508.
  • , & . . Predicting Spatio-Temporal Ambulance Demand: A Spatio-Temporal Kernel Approach. Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), 2297-2303.
  • , , , , & . . A Spatio-Temporal Point Process Model for Ambulance Demand. Journal of the American Statistical Association, 110(509), 6-15.
  • , , , , & . . An Evaluation of Independent Component Analyses with an Application to Resting State fMRI. Biometrics, 70(1), 224-236.
  • , , , , & . . Disability-Inclusive Employer Practices and Hiring of Individuals with Disabilities. Rehabilitation Education, 28(4), 309-328.
  • , & . . ecp: An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data. Journal of Statistical Software, 62(7), 1-25.
  • , & . . A Nonparametric Approach for Multiple Change Point Analysis of Multivariate Data. Journal of the American Statistical Association, 109(505), 334-345.
  • , , , & . . Locally Stationary Vector Processes and Adaptive Multivariate Modeling. Acoustics, Speech and Signal Processing, IEEE, 8722-8726.
  • , , , & . . Travel Time Estimation for Emergency Vehicles using Bayesian Data Augmentation. Annals of Applied Statistics, 7(2), 1139-1161.
  • , , , & . . An Approach for Identifying and Predicting Economic Recessions in Real-Time Using Time-Frequency Functional Models. Applied Stochastic Models in Business and Industry, 28(6), 485-499.
  • , & . . Dynamic Orthogonal Components for Multivariate Time Series. Journal of the American Statistical Association, 106(496), 1450-1463.
  • , , & . . Establishing Stationarity of Count-Valued Time Series Models using Drift Conditions. Electronic Journal of Statistics, 5, 800-828.
  • , , , & . . Forecasting Emergency Medical Service Call Arrival Rates. Annals of Applied Statistics, 5(2B), 1379-1406.
  • , & . . GARCH Models of Dynamic Volatility and Correlation. IEEE Signal Processing Magazine, 28(5), 72-82.

Software

  • , , , & . . EDMeasure -- Energy-Based Dependence Measures.
  • , , & . . bigtime -- A Package for Obtaining Sparse Estimates of Large Time Series Models.
  • , , & . . bigVAR – Dimension Reduction Methods for Multivariate Time Series.
  • , , & . . steadyICA – ICA and Tests of Independence via Multivariate Distance Covariance.
  • , & . . ecp – An R Package for Nonparametric Multiple Change Point Analysis of Multivariate Data.

Conference Proceedings

  • , , , & . . Interpretable Vector AutoRegressions with Exogenous Time Series.

Book Chapters

  • , , & . . Statistical Measures of Dependence For Financial Data. In Financial Signal Processing and Machine Learning. John Wiley & Sons.
  • , & . . Temporal and Spatio-Temporal Models for Ambulance Demand. In Healthcare Data Analysis. John Wiley & Sons.

Textbooks

  • , & . . Statistics and Data Analysis for Financial Engineering. Springer.

Honors and Awards

  • Best Paper Award, National Association of Rehabilitation Research and Training Centers Association.
  • Faculty Early Career Development (CAREER) Award, National Science Foundation.
  • Faculty Research Award, PARC/Xerox Foundation.
  • Best Academic Paper Award, R/Finance 2013, Applied Finance with R, International Center for Futures and Derivatives.
  • Best Academic Paper Award, R/Finance 2011, Applied Finance with R, International Center for Futures and Derivatives.
  • Paul Meier Fellowship, Department of Statistics, University of Chicago.
  • Statistical Consulting Award, Department of Statistics, University of Chicago.
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