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Multiple linear regression model with stochastic design variables

Author

Listed:
  • 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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    Cited by:

    1. 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., vol. 8(02), pages 1-18.
    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.

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