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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... -
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... -
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... -
Efficient Estimation for Semiparametric Structural Equation Models With Censo...
AbstractStructural equation modeling is commonly used to capture complex structures of relationships among multiple variables, both latent and observed. We propose a general... -
dedup_wf_001--cb7acff021f19cac7b51b389c088e9c7
Meta-analytic structural equation modeling (MASEM) is increasingly applied to advance theories by synthesizing existing findings. MASEM essentially consists of two stages. In... -
Semiparametric Models for Accelerated Destructive Degradation Test Data Analysis
Accelerated destructive degradation tests (ADDT) are widely used in industry to evaluate materials’ long-term properties. Even though there has been tremendous statistical... -
A Model of Text for Experimentation in the Social Sciences
Statistical models of text have become increasingly popular in statistics and computer science as a method of exploring large document collections. Social scientists often want... -
dedup_wf_001--34917c0ba753306b4050bc5e8c804ac5
In this paper, we investigate community detection in networks in the presence of node covariates. In many instances, covariates and networks individually only give a partial... -
dedup_wf_001--19af8529ec986c726fa0421aaaad4f03
This article proposes a new class of copula-based dynamic models for high-dimensional conditional distributions, facilitating the estimation of a wide variety of measures of... -
Selection of terms in random coefficient regression models
The selection of suitable terms in random coefficient regression models is a challenging problem to practitioners. Although many techniques, ranging from those with a... -
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,... -
Sequentially Refined Latin Hypercube Designs: Reusing Every Point
The use of iteratively enlarged Latin hypercube designs for running computer experiments has recently gained popularity in practice. This approach conducts an initial experiment...
