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Variable Selection for Skewed Model-Based Clustering: Application to the Iden...
In sleep research, applying finite mixture models to sleep characteristics captured through multiple data types, including self-reported sleep diary, a wrist monitor capturing... -
Embracing the Blessing of Dimensionality in Factor Models
Factor modeling is an essential tool for exploring intrinsic dependence structures among high-dimensional random variables. Much progress has been made for estimating the... -
Linear Model Selection When Covariates Contain Errors
Prediction precision is arguably the most relevant criterion of a model in practice and is often a sought after property. A common difficulty with covariates measured with... -
A Generalized Quasi-MMSE Controller for Run-to-Run Dynamic Models
This study proposes a generalized quasi-minimum mean square error (qMMSE) controller for implementing a run-to-run process control where the process input–output relationship... -
Sparse Multi-Dimensional Graphical Models: A Unified Bayesian Framework
Multi-dimensional data constituted by measurements along multiple axes have emerged across many scientific areas such as genomics and cancer surveillance. A common objective is... -
An Adaptive Exchange Algorithm for Sampling From Distributions With Intractab...
Sampling from the posterior distribution for a model whose normalizing constant is intractable is a long-standing problem in statistical research. We propose a new algorithm,... -
dedup_wf_001--05329493e4c2b3adc91e6f655708daa9
In applications of multivariate finite mixture models, estimating the number of unknown components is often difficult. We propose a bootstrap information criterion, whereby we... -
Scalable module detection for attributed networks with applications to breast...
The objective of network module detection is to identify groups of nodes within a network structure that are tightly connected. Nodes in a network often have attributes (aka... -
dedup_wf_001--65880abf0347d6b7b8d0628e70e3b6a0
Many environmental processes exhibit weakening spatial dependence as events become more extreme. Well-known limiting models, such as max-stable or generalized Pareto processes,... -
On the application of statistical learning approaches to construct inverse pr...
The marginal structural Cox model (MSCM) estimates can be highly sensitive to weight-model misspecification. We assess the performance of various popular statistical learners,...
