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Handbook of Computational Statistics pp 645–680Cite as

(Non) Linear Regression Modeling

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Abstract

We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables \(\mathbf{Y} = ({Y }_{1},\ldots,{Y }_{l}),l \in\mathbb{N}\), which are explained by a model, and independent (exogenous, explanatory) variables \(\mathbf{X} = ({X}_{1},\ldots,{X}_{p}),p \in\mathbb{N}\), which explain or predict the dependent variables by means of the model. Such relationships and models are commonly referred to as regression models.

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  1. Department of Econometrics & Operations Research, Tilburg University, Tilburg, The Netherlands

    Pavel Čı́žek

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  1. Dept. Computational & Data, Sciences, George Mason University, University Drive 4400, Fairfax, 22030-4444, Virginia, USA

    James E. Gentle

  2. L.v.Bortkiewicz Chair of Statistics, C.A.S.E. Centre f. Appl. Stat. & Econ., Humboldt-Universität zu Berlin, Unter den Linden 6, Berlin, 10099, Germany

    Wolfgang Karl Härdle

  3. Dept. Socioinformation, Okayama University, Ridai-cho 1-1, Okayama, 700-0005, Japan

    Yuichi Mori

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Čı́žek, P. (2012). (Non) Linear Regression Modeling. In: Gentle, J., Härdle, W., Mori, Y. (eds) Handbook of Computational Statistics. Springer Handbooks of Computational Statistics. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21551-3_23

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