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|a 9783642558641
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|a Groß, Jürgen
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| 245 |
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|a Linear Regression
|h Elektronische Ressource
|c by Jürgen Groß
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| 250 |
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|a 1st ed. 2003
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| 260 |
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|a Berlin, Heidelberg
|b Springer Berlin Heidelberg
|c 2003, 2003
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| 300 |
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|a XII, 398 p
|b online resource
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| 505 |
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|a I Point Estimation and Linear Regression -- Fundamentals -- The Linear Regression Model -- II Alternatives to Least Squares Estimation -- Alternative Estimators -- Linear Admissibility -- III Miscellaneous Topics -- The Covariance Matrix of the Error Vector -- Regression Diagnostics -- Matrix Algebra -- Stochastic Vectors -- An Example Analysis with R -- References
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| 653 |
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|a Statistical Theory and Methods
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| 653 |
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|a Statistics
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| 653 |
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|a Probability Theory and Stochastic Processes
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| 653 |
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|a Probabilities
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b SBA
|a Springer Book Archives -2004
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| 490 |
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|a Lecture Notes in Statistics
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| 856 |
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|u https://doi.org/10.1007/978-3-642-55864-1?nosfx=y
|x Verlag
|3 Volltext
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| 082 |
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|a 519.2
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| 520 |
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|a In linear regression the ordinary least squares estimator plays a central role and sometimes one may get the impression that it is the only reasonable and applicable estimator available. Nonetheless, there exists a variety of alterna tives, proving useful in specific situations. Purpose and Scope. This book aims at presenting a comprehensive survey of different point estimation methods in linear regression, along with the the oretical background on a advanced courses level. Besides its possible use as a companion for specific courses, it should be helpful for purposes of further reading, giving detailed explanations on many topics in this field. Numerical examples and graphics will aid to deepen the insight into the specifics of the presented methods. For the purpose of self-containment, the basic theory of linear regression models and least squares is presented. The fundamentals of decision theory and matrix algebra are also included. Some prior basic knowledge, however, appears to be necessary for easy reading and understanding
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