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Joint asymptotic distribution of marginal quantiles and quantile functions in samples from a multivariate population

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  • Babu, G. Jogesh
  • Rao, C. Radhakrishna

Abstract

The joint asymptotic distributions of the marginal quantiles and quantile functions in samples from a p-variate population are derived. Of particular interest is the joint asymptotic distribution of the marginal sample medians, on the basis of which tests of significance for population medians are developed. Methods of estimating unknown nuisance parameters are discussed. The approach is completely nonparametric.

Suggested Citation

  • Babu, G. Jogesh & Rao, C. Radhakrishna, 1988. "Joint asymptotic distribution of marginal quantiles and quantile functions in samples from a multivariate population," Journal of Multivariate Analysis, Elsevier, vol. 27(1), pages 15-23, October.
    • Handle: RePEc:eee:jmvana:v:27:y:1988:i:1:p:15-23
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    Citations

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    Cited by:

    1. Biman Chakraborty, 2001. "On Affine Equivariant Multivariate Quantiles," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(2), pages 380-403, June.
    2. Chitradipa Chakraborty & Subhra Sankar Dhar, 2020. "A Test for Multivariate Location Parameter in Elliptical Model Based on Forward Search Method," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 82(1), pages 68-95, February.
    3. Dominicy, Yves & Hörmann, Siegfried & Ogata, Hiroaki & Veredas, David, 2013. "On sample marginal quantiles for stationary processes," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 28-36.
    4. Hutson, Alan D., 2002. "Quasi-medians are robust and relatively efficient estimators of a common mean given multivariate normality," Statistics & Probability Letters, Elsevier, vol. 57(4), pages 403-408, May.
    5. Sankar, Subhra & Bergsma, Wicher & Dassios, Angelos, 2017. "Testing independence of covariates and errors in nonparametric regression," LSE Research Online Documents on Economics 83780, London School of Economics and Political Science, LSE Library.
    6. Jin Wang & Weihua Zhou, 2015. "Effect of kurtosis on efficiency of some multivariate medians," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 27(3), pages 331-348, September.
    7. G. Jogesh Babu & Ashish Mahabal, 2016. "Skysurveys, Light Curves and Statistical Challenges," International Statistical Review, International Statistical Institute, vol. 84(3), pages 506-527, December.
    8. Arie Beresteanu, 2016. "Quantile Regression with Interval Data," Working Paper 5991, Department of Economics, University of Pittsburgh.
    9. H. Barakat, 2001. "The Asymptotic Distribution Theory of Bivariate Order Statistics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 53(3), pages 487-497, September.
    10. Chung, EunYi & Romano, Joseph P., 2016. "Multivariate and multiple permutation tests," Journal of Econometrics, Elsevier, vol. 193(1), pages 76-91.
    11. Yves Dominicy & Hiroaki Ogata & David Veredas, 2013. "Inference for vast dimensional elliptical distributions," Computational Statistics, Springer, vol. 28(4), pages 1853-1880, August.
    12. Yves Dominicy & Siegfried Hörmann & David Veredas & Hiroaki Ogata, 2012. "Marginal quantiles for stationary processes," Working Papers 1228, Banco de España.
    13. Baumeister, Marléne & Ditzhaus, Marc & Pauly, Markus, 2024. "Quantile-based MANOVA: A new tool for inferring multivariate data in factorial designs," Journal of Multivariate Analysis, Elsevier, vol. 199(C).
    14. Hutson, Alan D., 2003. "Nonparametric estimation of normal ranges given one-way ANOVA random effects assumptions," Statistics & Probability Letters, Elsevier, vol. 64(4), pages 415-424, October.

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