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Shape Detection Using Semi-Parametric Shape-Restricted Mixed Effects Regression Spline with Applications
Linear models are widely used in the field of epidemiology to model the relationship between placental-fetal hormone and fetal/infant outcome. When a...
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Generalized Linear Mixed Models: Part I
For the most part, linear mixed models have been used in situations where the observations are continuous. However, oftentimes in practice the...
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Correspondence Analysis and Kriging: Projection of Quantitative Information on the Factorial Maps
In this study, a methodological scheme is proposed for the combined use of Analyse Factorielle des Correspondances—AFC (or Correspondence Analysis)...
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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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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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A Uniform Shrinkage Prior in Spatiotemporal Poisson Models for Count Data
We consider default Bayesian inference in a Poisson generalized linear mixed model for spatiotemporal data. Normal random effects are used to model...
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The Legend of the Equality of OLSE and BLUE: Highlighted by C. R. Rao in 1967
In this article, we go through some crucial developments regarding the equality of the ordinary least squares estimator and the best linear unbiased...
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Small area estimation with mixed models: a review
Small area estimation is recognized as an important tool for producing reliable estimates under limited sample information. This paper reviews...
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Optimal Design in Hierarchical Random Effect Models for Individual Prediction with Application in Precision Medicine
Hierarchical random effect models are used for different purposes in clinical research and other areas. In general, the main focus is on population...
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Inference of random effects for linear mixed-effects models with a fixed number of clusters
We consider a linear mixed-effects model with a clustered structure, where the parameters are estimated using maximum likelihood (ML) based on...
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Prediction in non-sampled areas under spatial small area models
In this article we study the prediction problem in small geographic areas in the situation where the survey data does not cover a substantial...
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Some Properties of Linear Prediction Sufficiency in the Linear Model
A linear statistic \(\mathbf {F}\mathbf {y}\)...
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Metaanalyse
Mithilfe einer Metaanalyse können die Ergebnisse mehrerer Studien zusammengefasst werden. Wir stellen nach einem Exkurs zur systematischen...
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Do Spatial Designs Outperform Classic Experimental Designs?
Controlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial...
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Fixed versus Mixed Effects Based Marginal Models for Clustered Correlated Binary Data: an Overview on Advances and Challenges
In a cross-sectional cluster setup, the binary responses from the individuals in a cluster become correlated as they share a common cluster effect,...
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Nested Error Regression Models
This chapter deals with the estimation of the regression and variance components’ parameters of the nested error regression model. It describes three...
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Exact distributions of statistics for making inferences on mixed models under the default covariance structure
At this juncture when mixed models are heavily employed in applications ranging from clinical research to business analytics, the purpose of this...
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Time Series I
This chapter is a largely empirical investigation of various aspects of the central England daily temperature series from Jan 1 1772 to Dec 31 2019,...
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Inference for Variance–Covariance Parameters
In Chaps. 11 and 13 , we obtained BLUEs for estimable linear functions under the Aitken model and BLUPs for predictable linear functions under...
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Small area estimation under a measurement error bivariate Fay–Herriot model
The bivariate Fay–Herriot model is an area-level linear mixed model that can be used for estimating the domain means of two correlated target...
