Independence test for high dimensional data based on regularized canonical correlation coefficients

By Yanrong Yang, Guangming Pan. 2015

54 citations recorded by the source corpus.

This paper proposes a new statistic to test independence between two high dimensional random vectors ${\mathbf{X}}:p_1\times1$ and ${\mathbf{Y}}:p_2\times1$. The proposed statistic is based on the sum of regularized sample canonical correlation coefficients of ${\mathbf{X}}$ and ${\mathbf{Y}}$. The asymptotic distribution of the statistic under the null hypothesis is established as a corollary of general central limit theorems (CLT) for the linear statistics of classical and regularized sample canonical correlation coefficients when $p_1$ and $p_2$ are both comparable to the sample size $n$. As applications of the developed independence test, various types of dependent structures, such as factor models, ARCH models and a general uncorrelated but dependent case, etc., are investigated by simulations. As an empirical application, cross-sectional dependence of daily stock returns of companies between different sections in the New York Stock Exchange (NYSE) is detected by the proposed test.

Subfields: Economics and Econometrics, Finance, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/cc36a254a2b783f624b85d6067e0bdaa8e84960e

How researchers see this paper

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In Economics and Econometrics

  1. Guangming Pan
  2. Z. Bao
  3. Wang Zhou

In Finance

  1. Guangming Pan
  2. Z. Bao
  3. Wang Zhou

In Statistics and Probability

  1. Z. Bao
  2. Guangming Pan
  3. Wang Zhou
  4. Jiang Hu
  5. Mathias Drton
  6. Xiucai Ding
  7. Jun Yin
  8. Fang Han

Citations in the corpus