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Oracle-efficient M-estimation for single-index models with a smooth simultaneous confidence band
Single-index models are important and popular semiparametric models, as they can handle the problem of the “curse of dimensionality” and enjoy the...
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Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic Distributions
Robust clustering of high-dimensional data is an important topic because clusters in real datasets are often heavy-tailed and/or asymmetric....
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Bayesian diagnostics in a partially linear model with first-order autoregressive skew-normal errors
This paper studies a Bayesian local influence method to detect influential observations in a partially linear model with first-order autoregressive...
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Empirical likelihood change point detection in quantile regression models
Quantile regression is an extension of linear regression which estimates a conditional quantile of interest. In this paper, we propose an empirical...
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Multivariate Leimkuhler Curve: Properties and Applications to Analysis of Bibliometric Data
The Leimkuhler curve has established itself as an efficient tool in the analysis and comparison of concentration of bibliometric measures of...
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Estimation and backtesting of risk measures with emphasis on distortion risk measures
Statistical methodology has an important role to play in risk measurement. In this paper, we will review and discuss some statistical issues on risk...
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Predicting species-level vegetation cover using large satellite imagery data sets
Accurate information on the distribution of vegetation species is used as a proxy for the health of an ecosystem, a currency of international...
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A Distributed Regression Analysis Application Package Using
SAS Distributed regression is a privacy-preserving analytical method that performs multiple regression analysis using only summary-level information from...
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Additive partial linear models with autoregressive symmetric errors and its application to the hospitalizations for respiratory diseases
Additive partial linear models with symmetric autoregressive errors of order p are proposed in this paper for modeling time series data....
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An Efficient Testing Procedure for High-Dimensional Mediators with FDR Control
The field of mediation analysis commonly explores the pathways that connect environmental exposures with health outcomes. With the development of...
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Profile quasi-maximum likelihood estimation for semiparametric varying-coefficient spatial autoregressive panel models with fixed effects
This paper aims to propose a profile quasi-maximum likelihood estimation method for semiparametric varying-coefficient spatial autoregressive(SVCSAR)...
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A spatio-temporal model for binary data and its application in analyzing the direction of COVID-19 spread
It is often of primary interest to analyze and forecast the levels of a continuous phenomenon as a categorical variable. In this paper, we propose a...
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Strong convergence of a nonparametric relative error regression estimator under missing data with functional predictors
In this paper, we develop a nonparametric estimator of the regression function for a functional explanatory variable and a scalar response variable...
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Functional Principal Component Analysis for Multiple Variables on Different Riemannian Manifolds
Functional principal component analysis (FPCA) is a very important dimension reduction tool for functional data analysis. The conventional FPCA...
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A Comparison of Estimation Methods for Shared Gamma Frailty Models
This paper compares six different estimation methods for shared frailty models via a series of simulation studies. A shared frailty model is a...
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Functional Causal Inference with Time-to-Event Data
Functional data analysis has proven to be a powerful tool for capturing and analyzing complex patterns and relationships in a variety of fields,...
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Asymptotic Behaviors of the VaR and CVaR Estimates for Widely Orthant Dependent Sequences
This paper considers some asymptotics of value-at-risk (VaR) and conditional value-at-risk (CVaR) estimates in the cases of extended negatively...
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A New Matrix Feature Selection Strategy in Machine Learning Models for Certain Krylov Solver Prediction
Numerical simulation processes in scientific and engineering applications require efficient solutions of large sparse linear systems, and variants of...
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Clustering with Minimum Spanning Trees: How Good Can It Be?
Minimum spanning trees (MSTs) provide a convenient representation of datasets in numerous pattern recognition activities. Moreover, they are...
