Join the Department of Statistics for a seminar featuring Debashis Mondal, associate professor in the Department of Statistics and Data Science at Washington University in St. Louis.

Abstract:

In this talk I develop a mathematical and statistical framework for analyzing spatial systems on irregularly distributed point locations. Using conditional formulations, this framework constructs Gaussian systems on spatial networks with symmetric weights for both stationary and non-stationary point processes. The framework associates these systems with reversible Markov chains and, through Kipnis--Varadhan invariance principles, establishes geostatistical scaling limits that recover the de Wijs process or Gaussian free field under stationary sampling and generalize to more complex limits under inhomogeneous sampling. The framework further studies convergence through Beurling--Deny decomposition, the effects of non-uniform sampling on limiting fields, and normalization procedures based on Hungarian embeddings, Sinkhorn normalization, and density correction for restoring canonical limits. It also enables scalable matrix-free computation, REML estimation, conditional simulation, spatial prediction, and sampling-invariant inference on large irregular spatial networks, for which there is currently little parallel in the geostatistical literature. The talk concludes with applications to mapping soil organic carbon in Tanzania, reconstructing global seawater oxygen isotope fields, and analyzing leukemia survival data from northwest England.

This seminar presents joint work with postdoctoral scholar Subhrajyoty Roy.

About the Speaker:

Debashis Mondal is an associate professor in the Department of Statistics and Data Science at Washington University in St. Louis. Previously, he was a faculty member at Oregon State University and the University of Chicago. He earned his Ph.D. in Statistics from the University of Washington. His research spans spatial statistics, computational science, and machine learning, with applications in agronomy, ecology, biodiversity, environmental and climate sciences, sports analytics, crime and police response, and digital humanities. He has received the NSF CAREER Award and the Young Researcher and a Service Awards from the International Indian Statistical Association and is an elected member of the International Statistical Institute.

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GWSB's 4+1 programs are open to undergraduate students across the University. See the 4+1 website for more details on any restrictions. → REGISTER HERE

**THIS IS FOR CURRENT GW STUDENTS ONLY**

View on site | Email this event

GWSB's 4+1 programs are open to undergraduate students across the University. See the 4+1 website for more details on any restrictions. → REGISTER HERE

**THIS IS FOR CURRENT GW STUDENTS ONLY**

View on site | Email this event

GWSB's 4+1 programs are open to undergraduate students across the University. See the 4+1 website for more details on any restrictions. → REGISTER HERE

**THIS IS FOR CURRENT GW STUDENTS ONLY**

View on site | Email this event

GWSB's 4+1 programs are open to undergraduate students across the University. See the 4+1 website for more details on any restrictions. → REGISTER HERE

**THIS IS FOR CURRENT GW STUDENTS ONLY**

View on site | Email this event

Entrepreneurs must be able to communicate their ideas clearly, concisely, and persuasively. This workshop teaches participants how to transform their business thesis into a compelling elevator pitch and introductory pitch deck. Students learn storytelling techniques and how to communicate their venture’s value in a way that resonates with customers, judges, mentors, and investors. This workshop helps students craft their elevator pitch for the Pitch George Competition hosted by the Center for Entrepreneurial Excellence.

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Please join the Statistics Department for a talk featuring Hui Zou, professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University.

The Box-Cox transformation paper ranks among the five most-cited articles in the Journal of the Royal Statistical Society, Series B. Bickel and Doksum (1981) revisited this classical model through analysis of the profile likelihood estimator and concluded that "the performance of all Box-Cox type procedures is unstable." We argue, however, that the scientific modeling philosophy of Box and Cox remains valuable in modern data analysis. To this end, we develop a novel composite likelihood framework for estimation and inference in a high-dimensional nonparametric Box-Cox (NBC) model. The composite likelihood approach directly resolves the instability concern of Bickel and Doksum (1981). Furthermore, NBC yields substantially improved prediction accuracy over standard regression, while preserving its core advantages: computational efficiency and model transparency.

Hui Zou's research interests include high-dimensional statistics, machine learning and statistical optimization. Zou is a recipient of the ICSA Pao-Lu Hsu award and ISI Founders of Statistics prize. He is an elected Fellow of IMS, ASA and AAAS.

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