论文标题

基于非负三角分布的独立圆形随机变量和最大似然圆形均匀性测试的总和

Sums of Independent Circular Random Variables and Maximum Likelihood Circular Uniformity Tests Based on Nonnegative Trigonometric Sums Distributions

论文作者

Fernández-Durán, José, Juan, Gregorio-Domínguez, Mercedes, María

论文摘要

单位圆上的圆形均匀分布在求和下封闭,即独立圆形均匀分布的随机变量的总和也是圆形均匀分布的。在这项研究中,结果表明,基于非负三角法(NNT)的圆形分布家族也已在总和下封闭。鉴于NNTS圆形分布的灵活性对多模式和偏度进行建模,这些是用于测试圆形均匀性的替代模型的良好候选者,以检测与圆形均匀性无效假设的不同偏差。圆形均匀分布是NNTS家族的成员,但是在NNTS参数空间中,它对应于参数空间边界的一个点,这意味着当使用最大似然方法估算参数时,不满足规则性条件。通过考虑标准化的最大似然估计器和广义似然比,开发了两个NNT测试圆形均匀性的测试。考虑到非规范条件,通过模拟获得了拟议的NNTS圆形均匀性测试的临界值,并通过拟合回归模型对任何样本量进行了插值。通过生成接近圆形均匀性null假设的NNT模型来评估所提出的NNT圆形均匀性测试的有效性。

The circular uniform distribution on the unit circle is closed under summation, that is, the sum of independent circular uniformly distributed random variables is also circular uniformly distributed. In this study, it is shown that a family of circular distributions based on nonnegative trigonometric sums (NNTS) is also closed under summation. Given the flexibility of NNTS circular distributions to model multimodality and skewness, these are good candidates for use as alternative models to test for circular uniformity to detect different deviations from the null hypothesis of circular uniformity. The circular uniform distribution is a member of the NNTS family, but in the NNTS parameter space, it corresponds to a point on the boundary of the parameter space, implying that the regularity conditions are not satisfied when the parameters are estimated by using the maximum likelihood method. Two NNTS tests for circular uniformity were developed by considering the standardised maximum likelihood estimator and the generalised likelihood ratio. Given the nonregularity condition, the critical values of the proposed NNTS circular uniformity tests were obtained via simulation and interpolated for any sample size by the fitting of regression models. The validity of the proposed NNTS circular uniformity tests was evaluated by generating NNTS models close to the circular uniformity null hypothesis.

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