Speaker: Chunming Zhang
Time: 10:30 am, June 24, Tuesday
Location: Online
Zoom Meeting ID: 881 0309 9426(Password:540575)
Abstract: Understanding sparse network dependencies among nodes from multivariate point process data has broad applications in information transmission, social science, and computational neuroscience. This paper introduces new continuous-time stochastic models for conditional intensity functions, revealing network structures within non-stationary multivariate counting processes. Our model’s stochastic mechanism is crucial for inferring graph parameters relevant to structure recovery, distinct from commonly used processes like the Poisson, Hawkes, queuing, and piecewise deterministic Markov processes. This leads to proposing a novel marked point process for intensity discontinuities. We derive concise representations of their conditional distributions and demonstrate cyclicity of the counting processes driven by recurrence time points. These theoretical properties enable us to establish statistical consistency and convergence properties for proposed penalized M-estimators in graph parameters under mild regularity conditions. Simulation evaluations showcase the method’s computational simplicity and improved estimation accuracy compared to existing approaches. Real neuron spike train recordings are analyzed to infer connectivity in neuronal networks.
Introduction to the speaker: Chunming Zhang is a Professor in the Department of Statistics at the University of Wisconsin–Madison. She earned a BS in Mathematical Statistics from Nankai University, an MS in Computational Mathematics from the Chinese Academy of Sciences, and a PhD in Statistics from the University of North Carolina at Chapel Hill. Her research focuses on statistical learning methods applied to computational neuroscience, bioinformatics, and financial econometrics, alongside the analysis of imaging, spatial, and temporal data. Her work also explores dimension reduction and high-dimensional inference, multiple hypothesis testing and large-scale simultaneous inference, nonparametric and semiparametric modeling and inference, functional and longitudinal data analysis, and change-point detection. Dr. Zhang is an elected Fellow of the Institute of Mathematical Statistics (IMS), an elected Fellow of the American Statistical Association (ASA), an elected Member of the International Statistical Institute (ISI), and a recipient of the IMS Medallion Award and Lecture (2024). She has also served on the editorial boards of the Annals of Statistics and the Journal of the American Statistical Association.
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