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DS-optimal designs for random coefficient first-degree regression model with heteroscedastic errors

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  • Wilk, M.
  • Zaigraev, A.

Abstract

Optimal design problems for random coefficient regression model with heteroscedastic errors are considered. Under continuous design setting, conditions for existence of 2-point design that is as good as a given k-point design and for existence of DS-optimal design are established.

Suggested Citation

  • Wilk, M. & Zaigraev, A., 2017. "DS-optimal designs for random coefficient first-degree regression model with heteroscedastic errors," Statistics & Probability Letters, Elsevier, vol. 128(C), pages 28-34.
    • Handle: RePEc:eee:stapro:v:128:y:2017:i:c:p:28-34
    DOI: 10.1016/j.spl.2017.04.016
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    References listed on IDEAS

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    1. Fu-Chuen Chang & Hung-Ming Lin, 2007. "On Minimally-supported D-optimal Designs for Polynomial Regression with Log-concave Weight Function," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 65(2), pages 227-233, February.
    2. Alexander Zaigraev, 2002. "Shape optimal design criterion in linear models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 56(3), pages 259-273, December.
    3. Lan Wang & Xiao-Hua Zhou, 2007. "Assessing the Adequacy of Variance Function in Heteroscedastic Regression Models," Biometrics, The International Biometric Society, vol. 63(4), pages 1218-1225, December.
    4. Zhao, Quanshui, 2001. "Asymptotically Efficient Median Regression In The Presence Of Heteroskedasticity Of Unknown Form," Econometric Theory, Cambridge University Press, vol. 17(4), pages 765-784, August.
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    Cited by:

    1. Cheng, Jing & Ai, Mingyao, 2020. "Optimal designs for panel data linear regressions," Statistics & Probability Letters, Elsevier, vol. 163(C).

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