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.
On the Identification of Trend and Correlation in Temporal and Spatial Regression by Ludwig Fahrmeir and Thomas Kneib 1
Estimating the Number of Clusters in Logistic Regression Clustering by an Information Theoretic Criterion by Guoqi Qian and C. Radhakrishna Rao and Yuehua Wu and Qing Shao 29
Quasi Score and Corrected Score Estimation in the Polynomial Measurement Error Model by Hans Schneeweiss 45
Estimation and Finite Sample Bias and MSE of FGLS Estimator of Paired Data Model by Weiqiang Qian and Aman Ullah 59
Prediction of Finite Population Total in Measurement Error Models by Hyang Mi Kim and A. K. Md. Ehsanes Saleh 79
The Vector Cross Product and 4x4 Skew-symmetric Matrices by Gotz Trenkler and Dietrich Trenkler 95
Simultaneous Prediction of Actual and Average Values of Response Variable in Replicated Measurement Error Models by Chandra Mani Paudel and Narinder Kumar 105
Local Sensitivity in the Inequality Restricted Linear Model by Huaizhen Qin and Alan T. K. Wan and Guohua Zou 135
Boosting Correlation Based Penalization in Generalized Linear Models by Jan Ulbricht and Gerhard Tutz 165
Simultaneous Prediction Based on Shrinkage Estimator by Anoop Chaturvedi and Suchita Kesarwani and Ram Chandra 181
Finite Mixtures of Generalized Linear Regression Models by Bettina Grun and Friedrich Leisch 205
Higher-order Dependence in the General Power ARCH Process and the Role of Power Parameter by Changli He and Hans Malmsten and Timo Terasvirta 231
Regression Calibration for Cox Regression Under Heteroscedastic Measurement Error - Determining Risk Factors of Cardiovascular Diseases from Error-prone Nutritional Replication Data by Thomas Augustin and Angela Doring and David Rummel 253
Homoscedastic Balanced Two-fold Nested Model when the Number of Sub-classes is Large by Shu-Min Liao and Michael Akritas 279
QR-Decomposition from the Statistical Point of View by Hilmar Drygas 293
On Penalized Least-Squares: Its Mean Squared Error and a Quasi-Optimal Weight Ratio by Burkhard Schaffrin 313
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