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Bivariate negative binomial regression model with excess zeros and right cens...
We propose a bivariate hurdle negative binomial (BHNB) regression model with right censoring to model correlated bivariate count data with excess zeros and few extreme... -
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... -
Volatility Martingale Difference Divergence Matrix and Its Application to Dim...
In this article, we propose the so-called volatility martingale difference divergence matrix (VMDDM) to quantify the conditional variance dependence of a random vector Y∈Rp... -
Testing for Uncorrelated Residuals in Dynamic Count Models With an Applicatio...
This article proposes new model checks for dynamic count models. Both portmanteau and omnibus-type tests for lack of residual autocorrelation are considered. The resulting test... -
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... -
A new estimation method for continuous threshold expectile model
The continuous threshold expectile regression model could capture the effect of a covariate on the response variable with two different straight lines, while intersecting an... -
dedup_wf_001--104bd21b45fed5b4f4998c5a7bb60fb2
A common approach to evaluating robustness to omitted variable bias is to observe coefficient movements after inclusion of controls. This is informative only if selection on... -
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... -
Using Response Times for Joint Modeling of Response and Omission Behavior
For adequate modeling of missing responses, a thorough understanding of the nonresponse mechanisms is vital. As a large number of major testing programs are in the process or... -
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,... -
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... -
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... -
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 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... -
Mini-Batch Metropolis–Hastings With Reversible SGLD Proposal
Traditional Markov chain Monte Carlo (MCMC) algorithms are computationally intensive and do not scale well to large data. In particular, the Metropolis–Hastings (MH) algorithm... -
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,... -
dedup_wf_001--123aa771b3cca9192e453b41bc8b7305
Dynamical interplays in emotions have been investigated using vector autoregressive (VAR) models, whose estimates can be used to cluster participants into unknown groups. The...
