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A maximum entropy type test of fit: Composite hypothesis case

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  • Lee, Sangyeol

Abstract

In this paper, we propose a goodness of fit test based on maximum entropy. As an extension of the result on the simple versus simple hypothesis case handled by Lee et al. (2011), a composite hypothesis case is taken into consideration. To eliminate the parameter estimation effect, we apply the Khmaladze transformation for the empirical process and obtain the asymptotic distribution of the proposed test. The performance of the test is investigated through Monte Carlo simulations.

Suggested Citation

  • Lee, Sangyeol, 2013. "A maximum entropy type test of fit: Composite hypothesis case," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 59-67.
  • Handle: RePEc:eee:csdana:v:57:y:2013:i:1:p:59-67
    DOI: 10.1016/j.csda.2012.06.006
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    References listed on IDEAS

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    1. Sangyeol Lee & Okyoung Na & Seongryong Na, 2003. "On the cusum of squares test for variance change in nonstationary and nonparametric time series models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 55(3), pages 467-485, September.
    2. Jushan Bai, 2003. "Testing Parametric Conditional Distributions of Dynamic Models," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 531-549, August.
    3. Bera, Anil K. & Jarque, Carlos M., 1981. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals : Monte Carlo Evidence," Economics Letters, Elsevier, vol. 7(4), pages 313-318.
    4. Lee, Sangyeol & Vonta, Ilia & Karagrigoriou, Alex, 2011. "A maximum entropy type test of fit," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2635-2643, September.
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    Cited by:

    1. Lee, Sangyeol & Oh, Haejune, 2015. "Entropy test and residual empirical process for autoregressive conditional duration models," Computational Statistics & Data Analysis, Elsevier, vol. 86(C), pages 1-12.

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