Time-Series Models of Dynamic Volatility and Correlation. - David S. Matteson; David Ruppert (IEEE Signal Process. Mag., 2011)

Locally stationary vector processes and adaptive multivariate modeling. - David S. Matteson; Nicholas A. James; William B. Nicholson; Louis C. Segalini (ICASSP, 2013)

Predicting Ambulance Demand: a Spatio-Temporal Kernel Approach. - Zhengyi Zhou; David S. Matteson (KDD, 2015)

Leveraging cloud data to mitigate user experience from 'breaking bad'. - Nicholas A. James; Arun Kejariwal; David S. Matteson (IEEE BigData, 2016)

Mixed data and classification of transit stops. - Laura L. Tupper; David S. Matteson; John C. Handley (IEEE BigData, 2016)

Large-network travel time distribution estimation for ambulances. - Bradford S. Westgate; Dawn B. Woodard; David S. Matteson; Shane G. Henderson (Eur. J. Oper. Res., 2016)

Spatiotemporal mixed modeling of multi-subject task fMRI via method of moments. - Benjamin B. Risk; David S. Matteson; R. Nathan Spreng; David Ruppert (NeuroImage, 2016)

Pruning and Nonparametric Multiple Change Point Detection. - Wenyu Zhang 003; Nicholas A. James; David S. Matteson (ICDM Workshops, 2017)

Testing for Conditional Mean Independence with Covariates through Martingale Difference Divergence. - Ze Jin; Xiaohan Yan; David S. Matteson (UAI, 2018)

Generalizing distance covariance to measure and test multivariate mutual dependence via complete and incomplete V-statistics. - Ze Jin; David S. Matteson (J. Multivar. Anal., 2018)

Band Depth Clustering for Nonstationary Time Series and Wind Speed Behavior. - Laura L. Tupper; David S. Matteson; C. Lindsay Anderson; Luckny Zéphyr (Technometrics, 2018)

Independent Component Analysis Based on Mutual Dependence Measures. - Ze Jin; David S. Matteson; Tianrong Zhang (ICMLA, 2019)

ABACUS: Unsupervised Multivariate Change Detection via Bayesian Source Separation. - Wenyu Zhang 003; Daniel E. Gilbert; David S. Matteson (SDM, 2019)

Optimization and testing in linear non-Gaussian component analysis. - Ze Jin; Benjamin B. Risk; David S. Matteson (Stat. Anal. Data Min., 2019)

Learning to Rank with Missing Data via Generative Adversarial Networks. - Grace Deng; Cuize Han; David S. Matteson (CoRR, 2020)

High Dimensional Forecasting via Interpretable Vector Autoregression. - William B. Nicholson; Ines Wilms; Jacob Bien; David S. Matteson (J. Mach. Learn. Res., 2020)

Graph-Based Continual Learning. - Binh Tang; David S. Matteson (ICLR, 2021)

Risk Identification & Quantification in Complex Human-Natural Systems via Convergent Data Intensive Research. - Toryn L. J. Schafer; Ryan M. McGranaghan; Mila Getmansky Sherman; Mei-Ling E. Feng; Olukunle O. Owolabi; Sean E. Ryan; Marie-Christine Düker; Michael Jauch; David S. Matteson (KDD, 2021)

Probabilistic Transformer For Time Series Analysis. - Binh Tang; David S. Matteson (NeurIPS, 2021)

Copula Quadrant Similarity for Anomaly Scores. - Matthew Davidow; David S. Matteson (CoRR, 2021)

AURORA: A Unified fRamework fOR Anomaly detection on multivariate time series. - Lin Zhang; Wenyu Zhang 003; Maxwell J. McNeil; Nachuan Chengwang; David S. Matteson; Petko Bogdanov (Data Min. Knowl. Discov., 2021)

PyXtal_FF: a python library for automated force field generation. - Howard Yanxon; David Zagaceta; Binh Tang; David S. Matteson; Qiang Zhu (Mach. Learn. Sci. Technol., 2021)

IB-GAN: A Unified Approach for Multivariate Time Series Classification under Class Imbalance. - Grace Deng; Cuize Han; Tommaso Dreossi; Clarence Lee; David S. Matteson (SDM, 2022)

Bayesian spillover graphs for dynamic networks. - Grace Deng; David S. Matteson (UAI, 2022)

Interpretable Latent Variables in Deep State Space Models. - Haoxuan Wu; David S. Matteson; Martin T. Wells (CoRR, 2022)

Group linear non-Gaussian component analysis with applications to neuroimaging. - Yuxuan Zhao; David S. Matteson; Stewart H. Mostofsky; Mary Beth Nebel; Benjamin B. Risk (Comput. Stat. Data Anal., 2022)

Extended missing data imputation via GANs for ranking applications. - Grace Deng; Cuize Han; David S. Matteson (Data Min. Knowl. Discov., 2022)

Factor analysis of mixed data for anomaly detection. - Matthew Davidow; David S. Matteson (Stat. Anal. Data Min., 2022)

Deep Denoising for Scientific Discovery: A Case Study in Electron Microscopy. - Sreyas Mohan; Ramon Manzorro; Joshua L. Vincent; Binh Tang; Dev Yashpal Sheth; Eero P. Simoncelli; David S. Matteson; Peter A. Crozier; Carlos Fernandez-Granda (IEEE Trans. Computational Imaging, 2022)

Graphical Influence Diagnostics for Changepoint Models. - Ines Wilms; Rebecca Killick; David S. Matteson (J. Comput. Graph. Stat., 2022)

Spatial correlation in weather forecast accuracy: a functional time series approach. - Phillip A. Jang; David S. Matteson (Comput. Stat., 2023)

Feature Detection and Hypothesis Testing for Extremely Noisy Nanoparticle Images using Topological Data Analysis. - Andrew M. Thomas; Peter A. Crozier; Yuchen Xu 007; David S. Matteson (Technometrics, 2023)

Dynamic Atomic Column Detection in Transmission Electron Microscopy Videos via Ridge Estimation. - Yuchen Xu 007; Andrew M. Thomas; Peter A. Crozier; David S. Matteson (IEEE Trans. Image Process., 2025)

Locally Adaptive Shrinkage Priors for Trends and Breaks in Count Time Series. - Toryn L. J. Schafer; David S. Matteson (Technometrics, 2025)

Drift versus Shift: Decoupling Trends and Changepoint Analysis. - Haoxuan Wu; Toryn L. J. Schafer; Sean E. Ryan; David S. Matteson (Technometrics, 2025)

Dynamic Bayesian Item Response Model with Decomposition (D-BIRD): Modeling Cohort and Individual Learning Over Time. - Hansol Lee; Jason B. Cho; David S. Matteson; Benjamin W. Domingue (CoRR, 2025)

Likelihood Inference for Possibly Nonstationary Processes via Adaptive Overdifferencing. - Maryclare Griffin; Gennady Samorodnitsky; David S. Matteson (Technometrics, 2025)