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DTSTAMP:20260519T101823
DTSTART;TZID=America/Detroit:20260526T153000
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SUMMARY:Lecture / Discussion:Structured Statistical Learning and Inference for Complex Scientific Data
DESCRIPTION:This dissertation develops structured statistical learning and inference methods for complex scientific data. Here\, structure refers to problem-specific patterns that can be modeled to improve learning or inference: cluster-specific abundance and presence--absence patterns in microbiome compositions\, modular organization in high-dimensional conditional dependence networks\, and the conditional predictive structure among outcomes\, covariates\, and black-box predictions. Modeling such structure can improve clustering\, network learning\, and inference while preserving interpretability and statistical validity.\n\nThe first part studies model-based clustering of microbiome compositional data. We develop an Ising-Dirichlet mixture model for zero-inflated compositions\, where each cluster has a presence--absence dependence structure and a nonzero abundance profile. The method is designed to improve clustering with limited samples by using information from both taxon occurrence patterns and relative abundance variation. Simulations and a resistant potato starch study show improved clustering accuracy and interpretable microbiome subgroups.\n\nThe second part studies variable clustering in high-dimensional graphical models. We develop a one-step joint estimation framework for a sparse precision matrix and a latent variable partition. This allows graph estimation and partition recovery to reinforce each other\, rather than clustering a separately estimated graph. The method treats the partition as an explicit estimation target and allows nonzero cross-cluster dependence\, relying on a modularity criterion in which within-cluster connectivity is denser than between-cluster connectivity. Simulations and real-data applications show more stable and interpretable graph-and-cluster representations than two-stage alternatives.\n\nThe third part studies statistical inference with limited gold-standard labels and abundant black-box predictions. Because these predictions are not ground truth\, valid use requires bias correction. We develop adaptive prediction-powered inference\, which learns a score-side adjustment from labeled data to approximate the variance-optimal conditional score adjustment through Taylor-based and ensemble-based constructions. Simulations and real-data examples show that the method preserves coverage while producing smaller confidence regions than existing prediction-powered and surrogate-adjustment methods.
UID:148340-21903957@events.umich.edu
URL:https://events.umich.edu/event/148340
CLASS:PUBLIC
STATUS:CONFIRMED
CATEGORIES:Dissertation
LOCATION:Off Campus Location
CONTACT:
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