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Seasonal Adjustment: Meaning, Purpose, and Methods
This chapter deals with the causes and characteristics of seasonality. For gradual changes in seasonality whether of a stochastic or a deterministic...
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Introduction
This chapter introduces the main topics of the book that are divided into two parts: (I) seasonal adjustment methods and (II) real time trend-cycle...
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Statistical Validation of Surrogate Markers in Clinical Trials
The increasing cost of drug development has raised the demand on the use of biomarkers as surrogate endpoints for the evaluation of new drugs in...
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Formulas Useful for Linear Regression Analysis and Related Matrix Theory
Linear model. By $$\fancyscript{M}= \{\varvec{\mathbf{y }}, \varvec{\mathbf{X...
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Mixed Models and Variance Components
Traditionally, linear models have been divided into three categories: fixed effects models, random effects models, and mixed models. The...
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General Solution to AYB = C
In almost every chapter of this book we meet linear equations whose explicit solutions we wish to write up. This is what generalized inverses make...
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BLUE
In this chapter we focus on the BLUE-related matters, so to say. The most important thing is the fundamental BLUE equation (10.4) (p. 216).
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Identifying Differentially Expressed Genes in Time Course Microarray Data
Identifying differentially expressed (DE) genes across conditions or treatments is a typical problem in microarray experiments. In time course...
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Equality of BLUEs or BLUPs under two linear models using stochastic restrictions
In this paper, we consider mixed linear models, possibly with singular covariance matrices, by supplementing a particular fixed effects model with...
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Quadratic estimators of covariance components in a multivariate mixed linear model
It is known that the Henderson Method III (Biometrics 9:226–252, 1953) is of special interest for the mixed linear models because the estimators of...
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The one-step-late PXEM algorithm
The EM algorithm is a popular method for computing maximum likelihood estimates or posterior modes in models that can be formulated in terms of...
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Hierarchical-Likelihood Approach for Mixed Linear Models with Censored Data
Mixed linear models describe the dependence via random effects in multivariate normal survival data. Recently they have received considerable...
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A nonlinear Gauss-Seidel algorithm for inference about GLMM
A nonlinear Gauss-Seidel type algorithm is proposed for computing the maximum posterior estimates of the random effects in a generalized linear mixed...