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Testing for Varying Dispersion in Exponential Family Nonlinear Models

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  • Bo-Cheng Wei
  • Jian-Qing Shi
  • Wing-Kam Fung
  • Yue-Qing Hu

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  • 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.
    • Handle: RePEc:spr:aistmt:v:50:y:1998:i:2:p:277-294
    DOI: 10.1023/A:1003491131768
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    References listed on IDEAS

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    1. Philip J. Smith & Daniel F. Heitjan, 1993. "Testing and Adjusting for Departures from Nominal Dispersion in Generalized Linear Models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(1), pages 31-41, March.
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    Cited by:

    1. Hongyu Miao & Hulin Wu & Hongqi Xue, 2014. "Generalized Ordinary Differential Equation Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1672-1682, December.
    2. 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.
    3. Cysneiros, Audrey H.M.A. & Ferrari, Silvia L.P., 2006. "An improved likelihood ratio test for varying dispersion in exponential family nonlinear models," Statistics & Probability Letters, Elsevier, vol. 76(3), pages 255-265, February.
    4. Omerovic, Sanela & Friedl, Herwig & Grün, Bettina, 2022. "Modelling Multiple Regimes in Economic Growth by Mixtures of Generalised Nonlinear Models," Econometrics and Statistics, Elsevier, vol. 22(C), pages 124-135.
    5. Xin-Yu Tian & Xincheng Shi & Cheng Peng & Xiao-Jian Yi, 2021. "A Reliability Growth Process Model with Time-Varying Covariates and Its Application," Mathematics, MDPI, vol. 9(8), pages 1-15, April.
    6. Vanegas, Luis Hernando & Rondón, Luz Marina & Cysneiros, Francisco José A., 2012. "Diagnostic procedures in Birnbaum–Saunders nonlinear regression models," Computational Statistics & Data Analysis, Elsevier, vol. 56(6), pages 1662-1680.
    7. Jin-Guan Lin & Li-Xing Zhu & Chun-Zheng Cao & Yong Li, 2011. "Tests of heteroscedasticity and correlation in multivariate t regression models with AR and ARMA errors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(7), pages 1509-1531, August.
    8. Artur J. Lemonte & Alexandre G. Patriota, 2011. "Influence diagnostics in Birnbaum--Saunders nonlinear regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(5), pages 871-884, February.
    9. Xia, Tian & Jiang, Xuejun & Wang, Xueren, 2015. "Strong consistency of the maximum quasi-likelihood estimator in quasi-likelihood nonlinear models with stochastic regression," Statistics & Probability Letters, Elsevier, vol. 103(C), pages 37-45.
    10. Crudu, Federico & Osorio, Felipe, 2020. "Bilinear form test statistics for extremum estimation," Economics Letters, Elsevier, vol. 187(C).
    11. Xie, Feng-Chang & Wei, Bo-Cheng & Lin, Jin-Guan, 2009. "Homogeneity diagnostics for skew-normal nonlinear regression models," Statistics & Probability Letters, Elsevier, vol. 79(6), pages 821-827, March.
    12. Feng-Chang Xie & Jin-Guan Lin & Bo-Cheng Wei, 2010. "Testing for varying zero-inflation and dispersion in generalized Poisson regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(9), pages 1509-1522.
    13. Baddeley, Adrian & Turner, Rolf & Mateu, Jorge & Bevan, Andrew, 2013. "Hybrids of Gibbs Point Process Models and Their Implementation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 55(i11).
    14. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2019. "Consequences of Model Misspecification for Maximum Likelihood Estimation with Missing Data," Econometrics, MDPI, vol. 7(3), pages 1-27, September.
    15. Jin-Guan Lin & Yan-Yong Zhao & Hong-Xia Wang, 2015. "Heteroscedasticity diagnostics in varying-coefficient partially linear regression models and applications in analyzing Boston housing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(11), pages 2432-2448, November.
    16. Chun-Zheng Cao & Jin-Guan Lin & Li-Xing Zhu, 2010. "Heteroscedasticity and/or autocorrelation diagnostics in nonlinear models with AR(1) and symmetrical errors," Statistical Papers, Springer, vol. 51(4), pages 813-836, December.
    17. 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.
    18. Ferrari, Silvia L.P. & Cysneiros, Audrey H.M.A., 2008. "Skovgaard's adjustment to likelihood ratio tests in exponential family nonlinear models," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 3047-3055, December.
    19. Patrícia L. Espinheira & Alisson Oliveira Silva, 2020. "Residual and influence analysis to a general class of simplex regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 523-552, June.
    20. Zhao, Weihua & Zhang, Riquan & Huang, Zhensheng & Feng, Jingyan, 2012. "Partially linear single-index beta regression model and score test," Journal of Multivariate Analysis, Elsevier, vol. 103(1), pages 116-123, January.
    21. Richard M. Golden & Steven S. Henley & Halbert White & T. Michael Kashner, 2016. "Generalized Information Matrix Tests for Detecting Model Misspecification," Econometrics, MDPI, vol. 4(4), pages 1-24, November.
    22. Feng-Chang Xie & Bo-Cheng Wei & Jin-Guan Lin, 2007. "Case-deletion Influence Measures for the Data from Multivariate t Distributions," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(8), pages 907-921.
    23. Luigi Augugliaro & Angelo M. Mineo & Ernst C. Wit, 2013. "Differential geometric least angle regression: a differential geometric approach to sparse generalized linear models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 75(3), pages 471-498, June.

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