Regression Models

Wolfgang Karl Härdle and Zdeněk Hlávka
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Wolfgang Karl Härdle: Humboldt-Universität zu Berlin, C.A.S.E. Centre f. Appl. Stat. & Econ. School of Business and Economics
Zdeněk Hlávka: Charles University in Prague, Faculty of Mathematics and Physics Department of Statistics

Chapter Chapter 8 in Multivariate Statistics, 2015, pp 141-156 from Springer

Abstract: Abstract In Chap. 3 , we have introduced the linear model Linear model 8.1 $$\displaystyle{ Y = \mathcal{X}\beta +\varepsilon, }$$ where Y denotes a (n × 1) random vector of observations of the response variable, $$\mathcal{X}$$ is the (n × r) design matrix containing the corresponding values of the explanatory variables, β is a (r × 1) vector of unknown parameters and $$\varepsilon$$ is a (n × 1) random vector such that $$\mathop{\mathrm{\mathsf{E}}}\nolimits \varepsilon = 0_{n}$$ and $$\mathop{\mathrm{\mathsf{Var}}}\nolimits \varepsilon =\sigma ^{2}\mathcal{I}_{n}$$ .

Keywords: Mean Square Error; Linear Regression Model; Design Matrix; Prediction Interval; Linear Predictor (search for similar items in EconPapers)
Date: 2015
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DOI: 10.1007/978-3-642-36005-3_8

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