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.Essays in Honour of Helge Toutenburg Shalabh, Christian Heumann ... yahoo. com 2 Department of Statistics, Tribhuvan University, P.N. Campus, Pokhra, Nepal ... Usually, when the least square estimators are used to construct the predictors, they yield the best linear unbiased ... For example, due to indirect measurements, practical difficulties, qualitative variables and proxy measurements etc., theanbsp;...

Title | : | Recent Advances in Linear Models and Related Areas |

Author | : | Shalabh, Christian Heumann |

Publisher | : | Springer Science & Business Media - 2008-07-11 |

Continue