Linear Regression [electronic resource] /

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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Main Authors: Groß, Jürgen. author., SpringerLink (Online service)
Format: Texto biblioteca
Language:eng
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2003
Subjects:Mathematics., Probabilities., Statistics., Probability Theory and Stochastic Processes., Statistical Theory and Methods.,
Online Access:http://dx.doi.org/10.1007/978-3-642-55864-1
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spelling KOHA-OAI-TEST:2129052018-07-30T23:46:40ZLinear Regression [electronic resource] / Groß, Jürgen. author. SpringerLink (Online service) textBerlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer,2003.engIn 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.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.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.Mathematics.Probabilities.Statistics.Mathematics.Probability Theory and Stochastic Processes.Statistical Theory and Methods.Springer eBookshttp://dx.doi.org/10.1007/978-3-642-55864-1URN:ISBN:9783642558641
institution COLPOS
collection Koha
country México
countrycode MX
component Bibliográfico
access En linea
En linea
databasecode cat-colpos
tag biblioteca
region America del Norte
libraryname Departamento de documentación y biblioteca de COLPOS
language eng
topic Mathematics.
Probabilities.
Statistics.
Mathematics.
Probability Theory and Stochastic Processes.
Statistical Theory and Methods.
Mathematics.
Probabilities.
Statistics.
Mathematics.
Probability Theory and Stochastic Processes.
Statistical Theory and Methods.
spellingShingle Mathematics.
Probabilities.
Statistics.
Mathematics.
Probability Theory and Stochastic Processes.
Statistical Theory and Methods.
Mathematics.
Probabilities.
Statistics.
Mathematics.
Probability Theory and Stochastic Processes.
Statistical Theory and Methods.
Groß, Jürgen. author.
SpringerLink (Online service)
Linear Regression [electronic resource] /
description 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.
format Texto
topic_facet Mathematics.
Probabilities.
Statistics.
Mathematics.
Probability Theory and Stochastic Processes.
Statistical Theory and Methods.
author Groß, Jürgen. author.
SpringerLink (Online service)
author_facet Groß, Jürgen. author.
SpringerLink (Online service)
author_sort Groß, Jürgen. author.
title Linear Regression [electronic resource] /
title_short Linear Regression [electronic resource] /
title_full Linear Regression [electronic resource] /
title_fullStr Linear Regression [electronic resource] /
title_full_unstemmed Linear Regression [electronic resource] /
title_sort linear regression [electronic resource] /
publisher Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer,
publishDate 2003
url http://dx.doi.org/10.1007/978-3-642-55864-1
work_keys_str_mv AT großjurgenauthor linearregressionelectronicresource
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