Multiple linear regression model with stochastic design variables

In a simple multiple linear regression model, the design variables have traditionally been assumed to be non-stochastic. In numerous real-life situations, however, they are stochastic and non-normal. Estimators of parameters applicable to such situations are developed. It is shown that these estimators are efficient and robust. A real-life example is given.

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Year of publication:	2010
Authors:	Islam, M. Qamarul ; Tiku, Moti
Published in:	Journal of Applied Statistics. - Taylor & Francis Journals, ISSN 0266-4763. - Vol. 37.2010, 6, p. 923-943
Publisher:	Taylor & Francis Journals
Subject:	correlation coefficient \| least squares \| linear regression \| modified maximum likelihood \| multivariate distributions \| non-normality \| random design

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Extent:	text/html
Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008674950