Recent Advances in Linear Models and Related Areas [electronic resource] : Essays in Honour of Helge Toutenburg /
Shalabh.editor.
Heumann, Christian.editor.
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.
Includes index.
On the Identification of Trend and Correlation in Temporal and Spatial Regression -- Estimating the Number of Clusters in Logistic Regression Clustering by an Information Theoretic Criterion -- Quasi Score and Corrected Score Estimation in the Polynomial Measurement Error Model -- Estimation and Finite Sample Bias and MSE of FGLS Estimator of Paired Data Model -- Prediction of Finite Population Total in Measurement Error Models -- The Vector Cross Product and 4 × 4 Skew-symmetric Matrices -- Simultaneous Prediction of Actual and Average Values of Response Variable in Replicated Measurement Error Models -- Local Sensitivity in the Inequality Restricted Linear Model -- Boosting Correlation Based Penalization in Generalized Linear Models -- Simultaneous Prediction Based on Shrinkage Estimator -- Finite Mixtures of Generalized Linear Regression Models -- Higher-order Dependence in the General Power ARCH Process and the Role of Power Parameter -- Regression Calibration for Cox Regression Under Heteroscedastic Measurement Error - Determining Risk Factors of Cardiovascular Diseases from Error-prone Nutritional Replication Data -- Homoscedastic Balanced Two-fold Nested Model when the Number of Sub-classes is Large -- QR-Decomposition from the Statistical Point of View -- On Penalized Least-Squares: Its Mean Squared Error and a Quasi-Optimal Weight Ratio -- Optimal Central Composite Designs for Fitting Second Order Response Surface Linear Regression Models -- Does Convergence Really Matter? -- OLS-Based Estimation of the Disturbance Variance Under Spatial Autocorrelation -- Application of Self-Organizing Maps to Detect Population Stratification -- Optimal Designs for Microarray Experiments with Biological and Technical Replicates -- Weighted Mixed Regression Estimation Under Biased Stochastic Restrictions -- Coin Tossing and Spinning - Useful Classroom Experiments for Teaching Statistics -- Linear Models in Credit Risk Modeling.
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.
Probabilities.
Statistics .
Economic theory.
Operations research.
Decision making.
Mathematical statistics.
Probability Theory and Stochastic Processes.
Statistical Theory and Methods.
Economic Theory/Quantitative Economics/Mathematical Methods.
Operations Research/Decision Theory.
Probability and Statistics in Computer Science.