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Multiple regression model averaging and the focused information criterion wit...
We consider multiple regression (MR) model averaging using the Focused Informati on Criterion (FIC). Our approach is motivated by the problem of implementing a mean-variance... -
Testing for correlation between two time series using a parametric bootstrap
We study the problem of determining if two time series are correlated in the mean and variance. Several test statistics, originally designed for determining the correlation... -
Reliability analysis of multicomponent stress–strength reliability from a bat...
In this paper, inference for a multicomponent stress–strength model is studied. When latent strength and stress random variables follow a bathtub-shaped distribution and the... -
On Design Orthogonality, Maximin Distance, and Projection Uniformity for Comp...
Space-filling designs are widely used in both computer and physical experiments. Column-orthogonality, maximin distance, and projection uniformity are three basic and popular... -
Exponentiality tests based on Basu characterization
This paper proposes and studies two new classes of tests for exponentiality. Both of them are based on Basu's characterization of the exponential distribution. The null... -
dedup_wf_001--e93a3145f21bd0165a149147fd3024d6
In cluster-randomized trials, investigators randomize clusters of individuals such as households, medical practices, schools or classrooms despite the unit of interest are the... -
Estimation of bias-corrected intraclass correlation coefficient for unbalance...
Intraclass correlation coefficient for data in a clustered study is traditionally estimated from a one-way random-effects model. This model assumes normality for the random... -
Bias Correction for Replacement Samples in Longitudinal Research
Missing data are commonly encountered problem in longitudinal research. One way researchers handle missing data is through the use of supplemental samples (i.e., the addition of... -
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... -
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... -
Two-Step Estimation of Incomplete Information Social Interaction Models with ...
In this study, we consider linear social interaction models under incomplete information that allow for missing outcome data due to sample selection. For model estimation,... -
Semiparametric efficient inferences for generalised partially linear models
In this paper, we consider semiparametric efficient inferences in the generalised partially linear models. A novel bias-corrected estimating procedure and a bias-corrected... -
Simultaneous Covariance Inference for Multimodal Integrative Analysis
Multimodal integrative analysis fuses different types of data collected on the same set of experimental subjects. It is becoming a norm in many branches of scientific research,... -
Network Competition and Team Chemistry in the NBA
Abstract–We consider a heterogeneous social interaction model where agents interact with peers within their own network but also interact with agents across other (non-peer)... -
Small area estimation of expenditure means and ratios under a unit-level biva...
Under a unit-level bivariate linear mixed model, this paper introduces small area predictors of expenditure means and ratios, and derives approximations and estimators of the... -
dedup_wf_001--5616457cd6557bbe26d978211488ac58
The sample selection bias problem occurs when the outcome of interest is only observed according to some selection rule, where there is a dependence structure between the... -
Demand Models With Random Partitions
Many economic models of consumer demand require researchers to partition sets of products or attributes prior to the analysis. These models are common in applied problems when... -
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... -
Counterfactual Analysis and Inference with Nonstationary Data
Recently, there has been growing interest in developing econometric tools to conduct counterfactual analysis with aggregate data when a single “treated” unit suffers an...
