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A nonparametric measure of independence under a hypothesis of independent components

Author

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  • Rosenblatt, Murray
  • Wahlen, Bruce E.

Abstract

Asymptotic normality is derived for a nonparametric measure of independence of the components of random two-vectors. This result is obtained without the restrictive assumptions previuosly made on rate of convergence of the bandwidth sequence of the density estimates used.

Suggested Citation

  • Rosenblatt, Murray & Wahlen, Bruce E., 1992. "A nonparametric measure of independence under a hypothesis of independent components," Statistics & Probability Letters, Elsevier, vol. 15(3), pages 245-252, October.
    • Handle: RePEc:eee:stapro:v:15:y:1992:i:3:p:245-252
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    Citations

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    Cited by:

    1. Diks, C.G.H. & Panchenko, V., 2005. "Nonparametric Tests for Serial Independence Based on Quadratic Forms," CeNDEF Working Papers 05-13, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
    2. Bagkavos, D. & Patil, P.N., 2017. "A new test of independence for bivariate observations," Journal of Multivariate Analysis, Elsevier, vol. 160(C), pages 117-133.
    3. Luca Bagnato & Lucio De Capitani & Antonio Punzo, 2014. "Testing Serial Independence via Density-Based Measures of Divergence," Methodology and Computing in Applied Probability, Springer, vol. 16(3), pages 627-641, September.
    4. Pinkse, Joris, 1998. "A consistent nonparametric test for serial independence," Journal of Econometrics, Elsevier, vol. 84(2), pages 205-231, June.

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