(Non) linear regression modelling

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

Abstract

We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1,…,Yl), l ∈ N, which are explained by a model, and independent (exogenous, explanatory) variables X = (X1,…,Xp),p ∈ N, which explain or predict the dependent variables by means of the model. Such relationships and models are commonly referred to as regression models.
Original languageEnglish
Title of host publicationHandbook of Computational Statistics - 2nd Edition
EditorsJ.E. Gentle, W.K. Hardle, Y. Mori
Place of PublicationHeidelberg
PublisherSpringer Verlag
Pages645-680
ISBN (Print)9783642215
Publication statusPublished - 2012

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Nonlinear Regression
Modeling
Dependent
Model
Regression Model
Random variable
Predict
Relationships

Cite this

Cizek, P. (2012). (Non) linear regression modelling. In J. E. Gentle, W. K. Hardle, & Y. Mori (Eds.), Handbook of Computational Statistics - 2nd Edition (pp. 645-680). Heidelberg: Springer Verlag.
Cizek, P. / (Non) linear regression modelling. Handbook of Computational Statistics - 2nd Edition. editor / J.E. Gentle ; W.K. Hardle ; Y. Mori. Heidelberg : Springer Verlag, 2012. pp. 645-680
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Cizek, P 2012, (Non) linear regression modelling. in JE Gentle, WK Hardle & Y Mori (eds), Handbook of Computational Statistics - 2nd Edition. Springer Verlag, Heidelberg, pp. 645-680.

(Non) linear regression modelling. / Cizek, P.

Handbook of Computational Statistics - 2nd Edition. ed. / J.E. Gentle; W.K. Hardle; Y. Mori. Heidelberg : Springer Verlag, 2012. p. 645-680.

Research output: Chapter in Book/Report/Conference proceedingChapterScientificpeer-review

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Cizek P. (Non) linear regression modelling. In Gentle JE, Hardle WK, Mori Y, editors, Handbook of Computational Statistics - 2nd Edition. Heidelberg: Springer Verlag. 2012. p. 645-680