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Heteroscedasticity diagnostics for t linear regression models

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  • Jin-Guan Lin
  • Li-Xing Zhu
  • Feng-Chang Xie

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  • Jin-Guan Lin & Li-Xing Zhu & Feng-Chang Xie, 2009. "Heteroscedasticity diagnostics for t linear regression models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 70(1), pages 59-77, June.
    • Handle: RePEc:spr:metrik:v:70:y:2009:i:1:p:59-77
    DOI: 10.1007/s00184-008-0179-2
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    References listed on IDEAS

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    1. Murray Aitkin, 1987. "Modelling Variance Heterogeneity in Normal Regression Using GLIM," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 36(3), pages 332-339, November.
    2. Diblasi, Angela & Bowman, Adrian, 1997. "Testing for constant variance in a linear model," Statistics & Probability Letters, Elsevier, vol. 33(1), pages 95-103, April.
    3. J. L. Hutton & P. J. Solomon, 1997. "Parameter Orthogonality in Mixed Regression Models for Survival Data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(1), pages 125-136.
    4. Bo-Cheng Wei & Jian-Qing Shi & Wing-Kam Fung & Yue-Qing Hu, 1998. "Testing for Varying Dispersion in Exponential Family Nonlinear Models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(2), pages 277-294, June.
    5. Barroso, Lúcia P. & Cordeiro, Gauss M., 2005. "Bartlett corrections in heteroskedastic t regression models," Statistics & Probability Letters, Elsevier, vol. 75(2), pages 86-96, November.
    6. Jin-Guan Lin & Bo-Cheng Wei & Nan-Song Zhang, 2004. "Varying Dispersion Diagnostics for Inverse Gaussian Regression Models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 31(10), pages 1157-1170.
    7. Cysneiros, Francisco José A. & Paula, Gilberto A. & Galea, Manuel, 2007. "Heteroscedastic symmetrical linear models," Statistics & Probability Letters, Elsevier, vol. 77(11), pages 1084-1090, June.
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    Cited by:

    1. Jin-Guan Lin & Ji Chen & Yong Li, 2012. "Bayesian Analysis of Student t Linear Regression with Unknown Change-Point and Application to Stock Data Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 40(3), pages 203-217, October.
    2. Mariana C. Araújo & Audrey H. M. A. Cysneiros & Lourdes C. Montenegro, 2020. "Improved heteroskedasticity likelihood ratio tests in symmetric nonlinear regression models," Statistical Papers, Springer, vol. 61(1), pages 167-188, February.
    3. Kang-Ping Lu & Shao-Tung Chang, 2021. "Robust Algorithms for Change-Point Regressions Using the t -Distribution," Mathematics, MDPI, vol. 9(19), pages 1-28, September.
    4. Li, Ai-Ping & Xie, Feng-Chang, 2012. "Diagnostics for a class of survival regression models with heavy-tailed errors," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 4204-4214.

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