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Small area estimation of average compositions under multivariate nested error regression models
This paper investigates the small area estimation of population averages of unit-level compositional data. The new methodology transforms the...
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EBLUPs Under Nested Error Regression Models
This chapter treats the problem of predicting linear combinations of components of a finite population random vector. The linear parameters have the...
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Model-Based Clustering with Nested Gaussian Clusters
A dataset may exhibit multiple class labels for each observation; sometimes, these class labels manifest in a hierarchical structure. A textbook...
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EBLUPs Under Two-Fold Nested Error Regression Models
This chapter introduces the Henderson 3, maximum likelihood, and residual maximum likelihood methods for estimating the regression and variance...
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Multivariate Count Data Regression Models and Their Applications
Multivariate regression models based on multivariate discrete distributions will be defined and studied. Multivariate discrete distributions...
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EBPs Under Nested Error Regression Models
This chapter derives empirical best predictors of additive parameters based on nested error regression models and pays special attention to the...
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Jackknife model averaging for linear regression models with missing responses
We consider model averaging estimation problem in the linear regression model with missing response data, that allows for model misspecification....
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The Growth Curve Model and Reduced-Rank Regression Methods
One additional general model class that has aspects of reduced-rank regression, especially in its mathematical structure, is that of the growth curve...
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Quantile regression in random effects meta-analysis model
In meta-analysis model, due to the appearance of publication bias or outliers, as well as the small sample size, the normal assumption is usually...
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Adaptive bi-level variable selection for multivariate failure time model with a diverging number of covariates
In this study we propose an adaptive bi-level variable selection method to analyze multivariate failure time data. In the regression setting, we...
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A Comparison of Extreme Gradient and Gaussian Process Boosting for a Spatial Logistic Regression on Satellite Data
A popular and successful method of obtaining regression models using decision tree learners is XGBoost. However, the method implicitly assumes...
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Two-Step Practical Screening Method for Cancer Gene Diagnoses—Multivariate Oncogenes Among 169 Microarrays
If physicians analyze their microarrays or RNA by my practical 2-step screening method (Method3), they obtain many “vital BGSs with a few genes and...
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Seemingly unrelated clusterwise linear regression for contaminated data
Clusterwise regression is an approach to regression analysis based on finite mixtures which is generally employed when sample observations come from...
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Joint Modeling of Geometric Features of Longitudinal Process and Discrete Survival Time Measured on Nested Timescales: An Application to Fecundity Studies
In biomedical studies, longitudinal processes are collected till time-to-event, sometimes on nested timescales (example, days within months). Most of...
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Multivariate Hidden Markov Models
This chapter provides three extended example analyses, applying hidden Markov models to multivariate time series. The first example (Sect. 6.1)...
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Robust Regression Estimators
A fundamental goal is understanding the nature of the association between some variable Y and a collection of explanatory variables...
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Analysis and asymptotic theory for nested case–control designs under highly stratified proportional hazards models
Nested case–control sampled event time data under a highly stratified proportional hazards model, in which the number of strata increases...
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A Step-Wise Multiple Testing for Linear Regression Models with Application to the Study of Resting Energy Expenditure
Motivated by the mechanistic model of the resting energy expenditure, we present a new multiple hypothesis testing approach to evaluate...
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Multivariate Normal Distribution
IN THIS CHAPTER, we generalize the bivariate normal distribution from the previous chapter to an arbitrary number of dimensions. We also make use of...
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Bayesian ridge estimators based on copula-based joint prior distributions for regression coefficients
Ridge regression is a widely used method to mitigate the multicollinearly problem often arising in multiple linear regression. It is well known that...
