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Multiple linear regression model with stochastic design variables

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  • M. Qamarul Islam
  • Moti Tiku

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.

Suggested Citation

  • M. Qamarul Islam & Moti Tiku, 2010. "Multiple linear regression model with stochastic design variables," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(6), pages 923-943.
  • Handle: RePEc:taf:japsta:v:37:y:2010:i:6:p:923-943 DOI: 10.1080/02664760902939612
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Hammoudeh, Shawkat & McAleer, Michael, 2013. "Risk management and financial derivatives: An overview," The North American Journal of Economics and Finance, Elsevier, pages 109-115.
    3. repec:wsi:afexxx:v:08:y:2013:i:02:n:s2010495213500073 is not listed on IDEAS
    4. GUORUI BIAN & MICHAEL McALEER & WING-KEUNG WONG, 2013. "Robust Estimation And Forecasting Of The Capital Asset Pricing Model," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., pages 1-18.
    5. 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.

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