Testing for independence by the empirical characteristic function
TLDR
In this paper, the empirical characteristic function (EMF) was used to test the total independence of d (≥2) random variables using empirical characteristic functions, parallel to that of Hoeffding, Blum, Kiefer and Rosenblatt.About:
This article is published in Journal of Multivariate Analysis.The article was published on 1985-06-01 and is currently open access. It has received 77 citations till now. The article focuses on the topics: Independence (mathematical logic) & Random variable.read more
Citations
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Journal ArticleDOI
Hypothesis Testing in Time Series via the Empirical Characteristic Function: A Generalized Spectral Density Approach
TL;DR: In this paper, the generalized spectral density is indexed by frequency and a pair of auxiliary parameters, which can capture all pairwise dependencies, including those with zero autocorrelation.
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An omnibus test for the two-sample problem using the empirical characteristic function
T. W. Epps,Kenneth J. Singleton +1 more
TL;DR: The empirical characteristic function (CF) as mentioned in this paper is the Fourier transform of the sample distribution function, the values of its real and imaginary parts at some real number t are merely sample means of cosine and sine functions of the data, the observations being multiplied by t. The power of the CF test compares favorably with that of competing omnibus tests when the data are continuous.
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A Consistent Test for Bivariate Dependence
TL;DR: In this article, a new and consistent rank test for bivariate dependence is developed, which can be obtained by removing the first Haijek projection from the quantity Sn-2 I'- XXk' I Y- YkJ.
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Recent and classical tests for exponentiality: a partial review with comparisons
TL;DR: In this paper, a wide selection of classical and recent tests for exponentiality are discussed and compared and a new goodness-of-fit test utilizing a novel characterization of the exponential distribution through its characteristic function is proposed.
Posted Content
A new coefficient of correlation
TL;DR: In this article, the authors define a simple and interpretable measure of the degree of dependence between the variables, which is 0 if and only if the variables are independent and 1 if one is a measurable function of the other, and has a simple asymptotic theory under the hypothesis of independence.
References
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Limit Theorems for Stochastic Processes
TL;DR: The authors consider a sequence of processes such that the multivariate distribution of the processes of the process (i.e., the process of finding the distribution of a set of processes) tends to the multi-dimensional distribution of that process.
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A Non-Parametric Test of Independence
TL;DR: In this article, a test for the independence of two random variables with continuous distribution function (d.f.) is proposed, which is consistent with respect to the class Ω′of d.f.
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Distribution Free Tests of Independence Based on the Sample Distribution Function
TL;DR: In this paper, the characteristic functions of the limiting d.f.s of a class of such test criteria are obtained, and the corresponding d. f. is tabled in the bivariate case, where the test is equivalent to one originally proposed by Hoeffding [4].
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A Quadratic Measure of Deviation of Two-Dimensional Density Estimates and A Test of Independence
TL;DR: In this article, estimates of multidimensional density functions based on a bounded and bandlimited weight function are considered and the asymptotic behavior of quadratic functions of density function estimates useful in setting up a test of goodness of fit of the density function is determined.
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An asymptotic decomposition for multivariate distribution-free tests of independence
Paul Deheuvels,Paul Deheuvels +1 more
TL;DR: In this article, it was shown that the empirical dependence function can be canonically separated into a finite set of independent Gaussian processes, enabling one to test the existence of dependence relationships within each subset of coordinates independently (in an asymptotic way) of what occurs in other subsets.