Statistical Network Analysis: Estimation and Inference
Abstract: Recent advances in computing and measurement technologies have led to an explosion in the amount of data with network structures in a variety of…
Abstract: Recent advances in computing and measurement technologies have led to an explosion in the amount of data with network structures in a variety of…
Abstract: Estimating dynamic treatment effects is essential across various disciplines, offering nuanced insights into the time-dependent causal impact of interventions. However, this estimation presents challenges…
Abstract: Respondent-driven sampling (RDS) is a network-based sampling strategy used to study hidden populations for which no sampling frame is available. In each epoch of…
Abstract: While right-censored time-to-event outcomes have been studied for decades, handling time-to-event covariates, also known as censored covariates, is now of growing interest. So far,…
Abstract: Graphs and networks are widely used to represent complex systems such as genetic regulatory networks, brain connectivity networks, etc. Learning underlying graphs from high-dimensional…
Welcome back for the 2024-25 academic year! During this seminar I will highlight department, faculty, and student achievements from our past year and welcome our…
Abstract: Gaussian graphical regression is a powerful means that regresses the precision matrix of a Gaussian graphical model on covariates, permitting the numbers of the…
Abstract: Decoding strategies play a pivotal role in text generation for modern language models, yet a perplexing gap persists between theory and practice. Surprisingly, strategies…
Abstract: To optimize mobile health interventions and advance domain knowledge on intervention design, it is critical to understand how the intervention effect varies over time…
Abstract: Many model-agnostic statistical diagnostics are based on repeatedly re-fitting a model with some observations deleted or replicated. Cross-validation, the non-parametric bootstrap, and outlier detection…
Abstract: With advances in data collection and storage, statistical learning algorithms are becoming increasingly popular for structure learning and prediction with large-scale data sets that…
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