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On Continuity of the Pearson Statistic and Sample Quantiles

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Abstract

Convergence with probability one (in probability) of sequences of the sample quantiles and the Pearson statistic that are formed by columns of N× n arrays of random variables and bivariate random vectors respectively is established, n→ ∞. Two applications for the continuity of the Pearson statistics, when sampling is only possible along a sequence converging to an inaccessible targeting value, are presented

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References

  • Billingsley P. (1995). Probability and Measure (3rd ed). Wiley, New York

    MATH  Google Scholar 

  • Cheng B.N., Rachev S.T. (1994). Multivariate stable future prices. Mathematical Finance 2:133–153

    Google Scholar 

  • Kruglov V.M. (2001). The asymptotic behavior of the Pearson statistic. Theory of Probability and its Applications 45:69–92

    Article  MathSciNet  Google Scholar 

  • Kuczmaszewska A. (2004). On some conditions for complete convergence for arrays. Statistics and Probability Letters 66: 399–405

    Article  MATH  MathSciNet  Google Scholar 

  • Madsen, H., Butts, M.B., Khu, S.T., Liong, S.Y. (2000). Data assimilation in rainfall-runoff forecasting. In: Proceedings 4th International Conference on Hydroinformatics, Iowa, USA, July 2000

  • Modarres R., Nolan J.P. (1994). A method for simulating stable random vectors. Computational Statistics 9:11–19

    MATH  MathSciNet  Google Scholar 

  • Nolan J.P., Panoraska A.K., McCulloch J.H. (2001). Estimation of stable spectral measures, stable non-Gaussian models in finance and econometrics. Mathematical and Computer Modelling 34:1113–1122

    Article  MATH  MathSciNet  Google Scholar 

  • Pourahmadi M. (1999). Joint mean covariance models with application to longitudinal data: unconstrained parameterisation. Biometrika 86:677–690

    Article  MATH  MathSciNet  Google Scholar 

  • Pourahmadi M. (2001). Foundation of Time Series Analysis and Prediction Theory. Wiley, New York

    Google Scholar 

  • Rachev S.T., Xin H. (1993). Test for association of random variables in the domain of attraction of multivariate stable law. Probability and Mathematical Statistics 14:125–141

    MATH  MathSciNet  Google Scholar 

  • Singer J.D., Willett, J.B. (2003). Applied longitudinal Data Analysis: Modelling Change and Event occurrence. Oxford University Press, New York

    Google Scholar 

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Correspondence to A. R. Soltani.

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Al-Jarallah, R.A., Soltani, A.R. & Al-Kandari, N.A. On Continuity of the Pearson Statistic and Sample Quantiles. Ann Inst Stat Math 58, 527–535 (2006). https://doi.org/10.1007/s10463-005-0028-2

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  • DOI: https://doi.org/10.1007/s10463-005-0028-2

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