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Advanced Methods of Statistical Process Control
Following Chap. 2 , we present in this chapter more advanced methods of statistical process control. We...
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The ridge prediction error sum of squares statistic in linear mixed models
In case of multicollinearity, PRESS statistics has been proposed to be used in the selection of the ridge biasing parameter of the ridge estimator...
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Growth Curves
This project deals with growth curves for arabidopsis plants of two strains. Over a period of 30 days, the height of each plant was recorded every...
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Optimal Designs for Prediction in Two Treatment Groups Random Coefficient Regression Models
The subject of this work is two treatment groups random coefficient regression models, in which observational units receive some group-specific...
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Linear Models
This chapter presents a short introduction to linear regression models with fixed effects. A proposition gives the explicit solutions of the...
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Mean Squared Error of EBLUPs
This chapter treats the problem of approximating and estimating the mean squared error of empirical best linear unbiased predictors of small area...
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Robust optimal designs using a model misspecification term
Much of classical optimal design theory relies on specifying a model with only a small number of parameters. In many applications, such models will...
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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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Hot Topics
This chapter is devoted to particular topics of interest as well as recent developments in OED. In particular, computer experiments, which is an...
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On Predicting Principal Components Through Linear Mixed Models
This work introduces a Principal Component Analysis of data given by the Best Predictor of a multivariate random vector. The mixed linear model...
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Criterion constrained Bayesian hierarchical models
The goal of this article is to improve the predictive performance of a Bayesian hierarchical statistical model by incorporating a criterion typically...
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Lineare Prognosemodelle
Zunächst wird die Prognoseaufgabe, wie "fehlende"Messwerte aus bereits bekannten Messwerten in einem Gebiet G ermittelt werden können, formuliert....
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Mixed-effect models with trees
Tree-based regression models are a class of statistical models for predicting continuous response variables when the shape of the regression function...
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mind, A methodology for multivariate small area estimation with multiple random effects
This paper describes a small area estimator based on a multivariate linear mixed model implemented in the R Package mind . The method is a...
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BLUE against OLSE in the location model: energy minimization and asymptotic considerations
The main purpose of the paper is to uncover the connections between kriging, energy minimization and properties of the ordinary least squares and...
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Exponential Bounds and Convergence Rates of Sieve Estimators for Functional Autoregressive Processes
In the following study, we deal with the exponential bounds and rates for a class of sieve estimators of Grenander for Functional Autoregressive...
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Sequential design of multi-fidelity computer experiments with effect sparsity
A growing area of focus is using multi-fidelity(MF) simulations to predict the behavior of complex physical systems. In order to adequately utilize...
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A Modified Bayesian Optimization Approach for Determining a Training Set to Identify the Best Genotypes from a Candidate Population in Genomic Selection
Training set optimization is a crucial factor affecting the probability of success for plant breeding programs using genomic selection....
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Three-fold Fay–Herriot model for small area estimation and its diagnostics
This paper introduces a three-fold Fay–Herriot model with random effects at three hierarchical levels. Small area best linear unbiased predictors of...
