Estimation and Regression for Sequentially-truncated Data
In observational cohort studies with complex sampling schemes, truncation arises when the time to event of interest is observed only when it falls below or…
In observational cohort studies with complex sampling schemes, truncation arises when the time to event of interest is observed only when it falls below or…
Multilayer networks continue to gain significant attention in many areas of study, particularly, due to their high utility in modeling interdependent systems such as critical…
Estimating large covariance/precision matrices are fundamental problems in modern multivariate statistics. Virtually all of the existing methods in this literature assume independent samples. In the…
When conducting analysis of electronic health records (EHR), oftentimes the data utilized is patient level data which readily allows for statistical analyses that properly adjust…
Abstract: Parametric models for networks with heterogeneity and/or complex dependence have seen considerable progress over the past two decades, opening the door to further modeling…
Abstract: We propose a model to flexibly estimate joint tail properties by exploiting the convergence of an appropriately scaled point cloud onto a compact limit…
Abstract: Remote sensing data sets produced by NASA and other space agencies are a vast resource for the study of climate change and the physical…
Abstract: The human microbiome is the collection of microorganisms that live on and inside of our bodies. Microbiome data are inherently challenging to analyze due…
Abstract: In typical single-cell RNA-seq (scRNA-seq) data analysis, a clustering algorithm is applied to find discrete cell clusters as putative cell types, and then a…
Abstract: Testing a global null is a canonical problem in statistics and has a wide range of applications. In view of the fact of no…
Abstract: We characterize functional brain imaging data, obtained via electroencephalograms, as functional data. Due to the highly observed levels of signal heterogeneity, functional regression analysis…
Abstract: Hidden Markov models are a popular class of time series models where the observation process depends on a latent state process taken to evolve…