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Fixed Effects Testing in High-Dimensional Linear Mixed Models
Many scientific and engineering challenges—ranging from pharmacokinetic drug dosage allocation and personalized medicine to marketing mix (4Ps) recommendations—require an... -
Factor Copula Models for Replicated Spatial Data
We propose a new copula model that can be used with replicated spatial data. Unlike the multivariate normal copula, the proposed copula is based on the assumption that a common... -
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... -
Leverage, Asymmetry, and Heavy Tails in the High-Dimensional Factor Stochasti...
We develop a flexible modeling and estimation framework for a high-dimensional factor stochastic volatility (SV) model. Our specification allows for leverage effects, asymmetry... -
Hidden Population Size Estimation From Respondent-Driven Sampling: A Network ...
Estimating the size of stigmatized, hidden, or hard-to-reach populations is a major problem in epidemiology, demography, and public health research. Capture–recapture and... -
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... -
Identifiability of Normal and Normal Mixture Models with Nonignorable Missing...
Missing data problems arise in many applied research studies. They may jeopardize statistical inference of the model of interest, if the missing mechanism is nonignorable, that... -
Decentralized nonparametric multiple testing
Consider a big data multiple testing task, where, due to storage and computational bottlenecks, one is given a very large collection of p-values by splitting into manageable... -
dedup_wf_001--766256c500066e34cf5b8b2ba73cf2e1
Unstructured data refers to information that lacks certain structures and cannot be organized in a predefined fashion. Unstructured data often involves words, texts, graphs,... -
Fitting competing risks data to bivariate Pareto models
This paper revisits two bivariate Pareto models for fitting competing risks data. The first model is the Frank copula model, and the second one is a bivariate Pareto model... -
A Bayesian Approach to Graphical Record Linkage and Deduplication
We propose an unsupervised approach for linking records across arbitrarily many files, while simultaneously detecting duplicate records within files. Our key innovation involves... -
Tensor Envelope Partial Least-Squares Regression
ABSTRACTPartial least squares (PLS) is a prominent solution for dimension reduction and high-dimensional regressions. Recent prevalence of multidimensional tensor data has led... -
A signature enrichment design with Bayesian adaptive randomization
Clinical trials in the era of precision cancer medicine aim to identify and validate biomarker signatures which can guide the assignment of individually optimal treatments to... -
A Group-Specific Recommender System
In recent years, there has been a growing demand to develop efficient recommender systems which track users’ preferences and recommend potential items of interest to users. In... -
A fuzzy clustering approach to evaluate individual competencies from REFLEX data
We empirically illustrate how concepts and methods involved in a grade of membership (GoM) analysis can be used to sort individuals by competence. Our study relies on a data set... -
Multi-level models can benefit from minimizing higher-order variations: an il...
This study aims to measure the robustness of multi-level models designed for three anthropometric indices – height-for-age (HAZ), weight-for-age (WAZ) and weight-for-height... -
Bayesian hierarchical joint modeling using skew-normal/independent distributions
The multiple longitudinal outcomes collected in many clinical trials are often analyzed by multilevel item response theory (MLIRT) models. The normality assumption for the... -
dedup_wf_001--afa70f3913a4a8a21446ba69eabdc594
Bayesian graphical models are a useful tool for understanding dependence relationships among many variables, particularly in situations with external prior information. In... -
Estimating a Parametric Component Lifetime Distribution from a Collection of ...
Maintenance data can be used to make inferences about the lifetime distribution of system components. Typically, a fleet contains multiple systems. Within each system, there is... -
Dynamical System Modeling of Self-Regulated Systems Undergoing Multiple Excit...
This article proposes a dynamical system modeling approach for the analysis of longitudinal data of self-regulated homeostatic systems experiencing multiple excitations. It...
