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stat.ME2026
Censored Graphical Horseshoe: Bayesian sparse precision matrix estimation with censored and missing data
The Tien Mai, Sayantan Banerjee
Gaussian graphical models provide a powerful framework for studying conditional dependencies in multivariate data, with widespread applications spanning biomedical, environmental s…
stat.ME2025
A self-supervised learning approach for denoising autoregressive models with additive noise: finite and infinite variance cases
Sayantan Banerjee, Agnieszka Wylomanska, Sundar S
The autoregressive time series model is a popular second-order stationary process, modeling a wide range of real phenomena. However, in applications, autoregressive signals are oft…