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Recent Advances in Linear Models and Related Areas: Essays in Honour of Helge Toutenburg

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The theory of linear models and regression analysis plays an essential role in the development of methods for the statistical modelling of data.

The book presents the most recent developments in the theory and applications of linear models and related areas of active research. The contributions include topics such as boosting, Cox regression models, cluster analysis, design of experiments, feasible generalized least squares, information theory, matrix theory, measurement error models, missing data models, mixture models, panel data models, penalized least squares, prediction, regression calibration, spatial models and time series models.

Several contributions illustrate applications in biomedical research, economics, finance, genetic epidemiology and medicine.

Author(s): Shalabh, Christian Heumann
Edition: 1
Publisher: Physica-Verlag HD
Year: 2008

Language: English
Pages: 448

Cover......Page 1
Recent Advances in Linear Models and Related Areas......Page 2
ISBN 978-3-7908-2063-8......Page 4
Preface......Page 5
Contents......Page 7
List of Contributors......Page 10
On the Identification of Trend and Correlation in Temporal and Spatial Regression......Page 15
Estimating the Number of Clusters in Logistic Regression Clustering by an Information Theoretic Criterion......Page 42
Quasi Score and Corrected Score Estimation in the Polynomial Measurement Error Model......Page 57
Estimation and Finite Sample Bias and MSE of FGLS Estimator of Paired Data Model......Page 71
Prediction of Finite Population Total in Measurement Error Models......Page 91
The Vector Cross Product and 4×4 Skew-symmetric Matrices......Page 106
Simultaneous Prediction of Actual and Average Values of Response Variable in Replicated Measurement Error Models......Page 116
Local Sensitivity in the Inequality Restricted Linear Model......Page 145
Boosting Correlation Based Penalization in Generalized Linear Models......Page 174
Simultaneous Prediction Based on Shrinkage Estimator......Page 190
Finite Mixtures of Generalized Linear Regression Models......Page 214
Higher-order Dependence in the General Power ARCH Process and the Role of Power Parameter......Page 240
Regression Calibration for Cox Regression Under Heteroscedastic Measurement Error — Determining Risk Factors of Cardiovascular Diseases from Error-prone Nutritional Replication Data......Page 261
Homoscedastic Balanced Two-fold Nested Model when the Number of Sub-classes is Large......Page 287
QR-Decomposition from the Statistical Point of View......Page 301
On Penalized Least-Squares: Its Mean Squared Error and a Quasi-Optimal Weight Ratio......Page 320
Optimal Central Composite Designs for Fitting Second Order Response Surface Linear Regression Models......Page 330
Does Convergence Really Matter?......Page 347
OLS-Based Estimation of the Disturbance Variance Under Spatial Autocorrelation......Page 362
Application of Self-Organizing Maps to Detect Population Stratification......Page 372
Optimal Designs for Microarray Experiments with Biological and Technical Replicates......Page 393
Weighted Mixed Regression Estimation Under Biased Stochastic Restrictions......Page 405
Coin Tossing and Spinning – Useful Classroom Experiments for Teaching Statistics......Page 421
Linear Models in Credit Risk Modeling......Page 431
Index......Page 444