Test of Bivariate Independence Based on Angular Probability Integral Transform with Emphasis on Circular-Circular and Circular-Linear Data
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arXiv
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| Format: | Preprint |
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2023
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| author | Fernández-Durán J., J. Gregorio-Domínguez M, M. |
| author_facet | Fernández-Durán J., J. Gregorio-Domínguez M, M. |
| contents | The probability integral transform (PIT) of a continuous random variable $X$ with distribution function $F_X$ is a uniformly distributed random variable $U=F_X(X)$. We define the angular probability integral transform (APIT) as $θ_U = 2 πU = 2 πF_{X}(X)$, which corresponds to a uniformly distributed angle on the unit circle. For circular (angular) random variables, the sum of absolutely continuous independent circular uniform random variables is a circular uniform random variable, that is, the circular uniform distribution is closed under summation, and it is a stable continuous distribution on the unit circle. If we consider the sum (difference) of the angular probability integral transforms of two random variables, $X_1$ and $X_2$, and test for the circular uniformity of their sum (difference), this is equivalent to the test of independence of the original variables. In this study, we used a flexible family of nonnegative trigonometric sums (NNTS) circular distributions, which include the uniform circular distribution as a member of the family, to evaluate the power of the proposed independence test; we complete this evaluation by generating samples from NNTS alternative distributions that may be at a closer proximity with respect to the circular uniform null distribution. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_02991 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Test of Bivariate Independence Based on Angular Probability Integral Transform with Emphasis on Circular-Circular and Circular-Linear Data Fernández-Durán J., J. Gregorio-Domínguez M, M. Methodology 62H11 The probability integral transform (PIT) of a continuous random variable $X$ with distribution function $F_X$ is a uniformly distributed random variable $U=F_X(X)$. We define the angular probability integral transform (APIT) as $θ_U = 2 πU = 2 πF_{X}(X)$, which corresponds to a uniformly distributed angle on the unit circle. For circular (angular) random variables, the sum of absolutely continuous independent circular uniform random variables is a circular uniform random variable, that is, the circular uniform distribution is closed under summation, and it is a stable continuous distribution on the unit circle. If we consider the sum (difference) of the angular probability integral transforms of two random variables, $X_1$ and $X_2$, and test for the circular uniformity of their sum (difference), this is equivalent to the test of independence of the original variables. In this study, we used a flexible family of nonnegative trigonometric sums (NNTS) circular distributions, which include the uniform circular distribution as a member of the family, to evaluate the power of the proposed independence test; we complete this evaluation by generating samples from NNTS alternative distributions that may be at a closer proximity with respect to the circular uniform null distribution. |
| title | Test of Bivariate Independence Based on Angular Probability Integral Transform with Emphasis on Circular-Circular and Circular-Linear Data |
| topic | Methodology 62H11 |
| url | https://arxiv.org/abs/2301.02991 |