(Non) linear regression modelling

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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
ISBN (Print)9783642215
Publication statusPublished - 2012


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.