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Maximizing adjusted covariance: new supervised dimension reduction for classification
This study proposes a new linear dimension reduction technique called Maximizing Adjusted Covariance (MAC), which is suitable for supervised...
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Importance-Performance Map Analysis of Capital Structure Using PLS-SEM: Evidence from Non-financial Sector
This study aims to identify the most important determinant that affects the capital structure decision of non-financial firms listed on the Pakistan...
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A higher-order life crafting scale validation using PLS-CCA: the Italian version
In this study, we highlight Life Crafting Scale (LCS) factor structure and model specifications by using partial least squares structural equations...
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Generalised least squares estimation of regularly varying space-time processes based on flexible observation schemes
Regularly varying stochastic processes model extreme dependence between process values at different locations and/or time points. For such stationary...
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Handling multicollinearity in quantile regression through the use of principal component regression
In many fields of applications, linear regression is the most widely used statistical method to analyze the effect of a set of explanatory variables...
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Multiple Change-Points Estimation in Linear Regression Models via an Adaptive LASSO Expectile Loss Function
In this paper, a linear model with possible change-points is considered. The errors do not satisfy the classical homoscedasticity assumption...
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To Survive or not to Survive: Findings from PLS-SEM on the Relationship Between Organizational Resources and Startups’ Survival
This study explores the phenomenon of startup survivalStartup survival in an incipient entrepreneurship ecosystem. For this purpose, multiple and...
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Univariate Consumption Study Applying Regression
This chapter introduces the standard univariate (or simple) linear regression model, called the ordinary least-squares model, which estimates the...
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Bayesian penalized Buckley-James method for high dimensional bivariate censored regression models
For high dimensional gene expression data, one important goal is to identify a small number of genes that are associated with progression of the...
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ROBOUT: a conditional outlier detection methodology for high-dimensional data
This paper presents a methodology, called ROBOUT, to identify outliers conditional on a high-dimensional noisy information set. In particular, ROBOUT...
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Multivariate Consumption Study Applying Regression
The last chapter covered multivariate linear regression—a model for predicting continuous response variables by applying a single predictor variable....
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Statistical inference of pth-order generalized binomial autoregressive model
To capture the higher-order autocorrelation structure for finite-range integer-valued time series of counts, and to consider the interdependence...
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PDAS: a Newton-type method for \(L_0\) regularized accelerated failure time model
Regularization methods are commonly utilized in survival analysis to address variable selection and estimation problems. Although most of the...
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On four-way CP model estimation efficiency
The latent structure of four-dimensional tensors can be investigated by means of the four-way CANDECOMP/PARAFAC model. This technique is seldom used...
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Fitting concentric elliptical shapes under general model
Fitting concentric ellipses is a crucial yet challenging task in image processing, pattern recognition, and astronomy. To address this complexity,...
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Alternative Approaches to Higher Order PLS Path Modeling: A Discussion on Methodological Issues and Applications
In the context of Partial Least Squares-Path Modeling (PLS-PM), higher-order constructs have enjoyed increasing popularity in the last few years in...
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Hierarchical Means Clustering
In the cluster analysis literature, there are several partitioning (non-hierarchical) methods for clustering multivariate objects based on model...
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Balanced Lattice Designs under Uncertain Environment
Balanced lattice designs are vital in numerous fields, especially in experimental design, where controlling variability among experimental units is...
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Mixed-effects location-scale model based on generalized hyperbolic distribution
Motivated by better modeling of intra-individual variability in longitudinal data, we propose a class of location-scale mixed-effects models, in...
