6,950 research outputs found
A Kernel Independence Test for Random Processes
A new non parametric approach to the problem of testing the independence of
two random process is developed. The test statistic is the Hilbert Schmidt
Independence Criterion (HSIC), which was used previously in testing
independence for i.i.d pairs of variables. The asymptotic behaviour of HSIC is
established when computed from samples drawn from random processes. It is shown
that earlier bootstrap procedures which worked in the i.i.d. case will fail for
random processes, and an alternative consistent estimate of the p-values is
proposed. Tests on artificial data and real-world Forex data indicate that the
new test procedure discovers dependence which is missed by linear approaches,
while the earlier bootstrap procedure returns an elevated number of false
positives. The code is available online:
https://github.com/kacperChwialkowski/HSIC .Comment: In Proceedings of The 31st International Conference on Machine
Learnin
Independence Test for High Dimensional Random Vectors
This paper proposes a new mutual independence test for a large number of high dimensional random vectors. The test statistic is based on the characteristic function of the empirical spectral distribution of the sample covariance matrix. The asymptotic distributions of the test statistic under the null and local alternative hypotheses are established as dimensionality and the sample size of the data are comparable. We apply this test to examine multiple MA(1) and AR(1) models, panel data models with some spatial cross-sectional structures. In addition, in a flexible applied fashion, the proposed test can capture some dependent but uncorrelated structures, for example, nonlinear MA(1) models, multiple ARCH(1) models and vandermonde matrices. Simulation results are provided for detecting these dependent structures. An empirical study of dependence between closed stock prices of several companies from New York Stock Exchange (NYSE) demonstrates that the feature of cross-sectional dependence is popular in stock marketsIndependence test, cross-sectional dependence, empirical spectral distribution, characteristic function, Marcenko-Pastur Law
Entropy-Based Independence Test
This paper presents a new test of independence (linear and non-linear) among distributions based on the entropy of
Shannon. The main advantages of the presented approach are the fact that this measure does not need to assume any type of
theoretical probability distribution and has the ability to capture the linear and non-linear dependencies, without requiring the
specification of any kind of dependence model
Kernel-based Conditional Independence Test and Application in Causal Discovery
Conditional independence testing is an important problem, especially in
Bayesian network learning and causal discovery. Due to the curse of
dimensionality, testing for conditional independence of continuous variables is
particularly challenging. We propose a Kernel-based Conditional Independence
test (KCI-test), by constructing an appropriate test statistic and deriving its
asymptotic distribution under the null hypothesis of conditional independence.
The proposed method is computationally efficient and easy to implement.
Experimental results show that it outperforms other methods, especially when
the conditioning set is large or the sample size is not very large, in which
case other methods encounter difficulties
Entropy-based independence test
WOS:000238021700039 (Nº de Acesso Web of Science)This paper presents a new test of independence (linear and non-linear) among distributions based on the entropy of Shannon. The main advantages of the presented approach are the fact that this measure does not need to assume any type of theoretical probability distribution and has the ability to capture the linear and non-linear dependencies, without requiring the specification of any kind of dependence model.info:eu-repo/semantics/acceptedVersio
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