Linear models and generalizations: least squares and alternatives

By:

Rao, C. Radhakrishna

Material type: Text

TextSeries: Springer series in statisticsPublication details: 2008 Springer BerlinEdition: 3rd extended edDescription: xix, 570 pISBN:

9783540742265

Subject(s):

Linear models (Statistics)

DDC classification:

519.92 R2L4

Summary: Thoroughly revised and updated with the latest results, this Third Edition provides an up-to-date account of the theory and applications of linear models. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions. They not only use least squares theory, but also alternative methods of estimation and testing based on convex loss functions and general estimating equations. (http://www.springer.com/statistics/statistical+theory+and+methods/book/978-3-540-74226-5)

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Item type	Current library	Call number	Status	Date due	Barcode	Item holds
Book	Ahmedabad	519.92 R2L4 (Browse shelf(Opens below))	Available		174588

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Previous	519.92 P2C6 Combinatorial optimization: algorithms and complexity	519.92 P3A9 Axiomatic bargaining game theory	519.92 P3I6 Introduction to optimization	519.92 R2L4 Linear models and generalizations: least squares and alternatives	519.92 R6C6 Convex analysis

Thoroughly revised and updated with the latest results, this Third Edition provides an up-to-date account of the theory and applications of linear models. The authors present a unified theory of inference from linear models and its generalizations with minimal assumptions. They not only use least squares theory, but also alternative methods of estimation and testing based on convex loss functions and general estimating equations. (http://www.springer.com/statistics/statistical+theory+and+methods/book/978-3-540-74226-5)

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