Linear Models and Generalizations : Least Squares and Alternatives /
By: Rao, C. Radhakrishna [author.].
Contributor(s): Shalabh [author.] | Toutenburg, Helge [author.] | Heumann, Christian [author.] | SpringerLink (Online service).
Material type:
BookSeries: Springer Series in Statistics: Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.Edition: Third Extended Edition.Description: XIX, 572 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540742272.Subject(s): Mathematics | Operations research | Decision making | Mathematical statistics | Probabilities | Statistics | Economic theory | Mathematics | Probability Theory and Stochastic Processes | Statistical Theory and Methods | Economic Theory/Quantitative Economics/Mathematical Methods | Probability and Statistics in Computer Science | Operation Research/Decision TheoryDDC classification: 519.2 Online resources: Click here to access online | Item type | Current location | Call number | Status | Date due | Barcode | Item holds |
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E books
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PK Kelkar Library, IIT Kanpur | Available | EBK10393 |
The Simple Linear Regression Model -- The Multiple Linear Regression Model and Its Extensions -- The Generalized Linear Regression Model -- Exact and Stochastic Linear Restrictions -- Prediction in the Generalized Regression Model -- Sensitivity Analysis -- Analysis of Incomplete Data Sets -- Robust Regression -- Models for Categorical Response Variables.
Thebookisbasedonseveralyearsofexperienceofbothauthorsinteaching linear models at various levels. It gives an up-to-date account of the theory and applications of linear models. The book can be used as a text for courses in statistics at the graduate level and as an accompanying text for courses in other areas. Some of the highlights in this book are as follows. A relatively extensive chapter on matrix theory (Appendix A) provides the necessary tools for proving theorems discussed in the text and o?ers a selectionofclassicalandmodernalgebraicresultsthatareusefulinresearch work in econometrics, engineering, and optimization theory. The matrix theory of the last ten years has produced a series of fundamental results aboutthe de?niteness ofmatrices,especially forthe di?erences ofmatrices, which enable superiority comparisons of two biased estimates to be made for the ?rst time. We have attempted to provide a uni?ed theory of inference from linear models with minimal assumptions. Besides the usual least-squares theory, alternative methods of estimation and testing based on convex loss fu- tions and general estimating equations are discussed. Special emphasis is given to sensitivity analysis and model selection. A special chapter is devoted to the analysis of categorical data based on logit, loglinear, and logistic regression models. The material covered, theoretical discussion, and a variety of practical applications will be useful not only to students but also to researchers and consultants in statistics.
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