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Multiple linear regression model with stochastic design variables

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  • M. Qamarul Islam
  • Moti Tiku
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    Abstract

    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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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Journal of Applied Statistics.

    Volume (Year): 37 (2010)
    Issue (Month): 6 ()
    Pages: 923-943

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    Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:923-943

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    Related research

    Keywords: correlation coefficient; least squares; linear regression; modified maximum likelihood; multivariate distributions; non-normality; random design;

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    Cited by:
    1. Guorui Bian & Michael McAleer & Wing-Keung Wong, 2010. "Robust Estimation and Forecasting of the Capital Asset Pricing Model," KIER Working Papers 735, Kyoto University, Institute of Economic Research.
    2. Khaleghei Ghosheh Balagh, Akram & Naderkhani, Farnoosh & Makis, Viliam, 2014. "Highway Accident Modeling and Forecasting in Winter," Transportation Research Part A: Policy and Practice, Elsevier, vol. 59(C), pages 384-396.
    3. Guorui Bian & Michael McAleer & Wing-Keung Wong, 2013. "Robust Estimation and Forecasting of the Capital Asset Pricing Model," Tinbergen Institute Discussion Papers 13-036/III, Tinbergen Institute.

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