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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...
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A Personal Celebration of Dr. D. Basu with Emphasis on Examples-Counterexamples-Clarifications
Preparing this centennial tribute to Dr. D. Basu (5 July, 1924 – 24 March, 2001) created an opportunity to selectively revisit a number of core...
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Robust variable selection for additive coefficient models
Additive coefficient models generalize linear regression models by assuming that the relationship between the response and some covariates is linear,...
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Scalable Bayesian p-generalized probit and logistic regression
The logit and probit link functions are arguably the two most common choices for binary regression models. Many studies have extended the choice of...
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Estimation in Multi-State Semi-Markov Models with a Cured Fraction and Masked Causes of Deaths
Analyses of disease-free survival data for certain cancer types indicate that cohorts of patients treated for cancer consist of individuals who are...
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Estimating Individualized Treatment Regimes to Optimize Incremental Cost-Effectiveness Ratio
Medical decision making can be challenging due to the trade-off between improving clinical efficacy and the associated medical costs. Evaluation of...
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Transient Analysis of a Modified Differentiated Vacation Queueing System for Energy-Saving in WiMAX
A modified differentiated vacation queueing system with a close-down period and impatient customers is investigated in this research paper. The...
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Forecasting multidimensional autoregressive time series model with symmetric \(\alpha\)-stable noise using artificial neural networks
Artificial neural networks have been widely studied and applied in time series forecasting. However, the existing studies focus more on the...
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Cluster Validation Based on Fisher’s Linear Discriminant Analysis
Cluster analysis aims to find meaningful groups, called clusters, in data. The objects within a cluster should be similar to each other and...
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A Class of Ratio and Product Types Estimators of Mean of a Sensitive Variable Using Ranked Set Sampling
In this paper, we introduce a novel ranked set generalized randomized response estimator designed for accurately estimating the mean of sensitive...
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Two-Part Mixed Effects Mixture Model for Zero-Inflated Longitudinal Compositional Data
Compositional data (CD) is mostly analyzed using ratios of components and log-ratio transformations to apply known multivariable statistical methods....
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DOLD: a digital platform for conducting online language experiments and surveys
Despite its potential for reducing costs, increasing efficiency, and expanding participant diversity, online data collection has not been widely...
