Non-parametric Procedures under Estimating Stochastic Dependencies in Nonlinear Systems

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• 作者：K.R. Chernyshov ^{myau@ipu.ru" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Stochastic systems ; Measures of dependence ; Strong consistency ; Nonlinearity ; Non-parametric estimates
• 刊名：IFAC-PapersOnLine
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：49
• 期：12
• 页码：1779-1784
• 全文大小：487 K

文摘

In the paper, a measure of dependence coupling k pairs of random processes is introduced. Such a measure, based on using conditional mathematical expectations of the processes, may be considered as a further generalization of the dispersion (variance) functions. Convergence with probability 1 of nonparametric estimates of such a measure is derived using sampled data. These estimates are applied to deriving sampled analogues of some nonlinear measures of stochastic dependence of random processes, in particular, to a consistent, in the Kolmogorov sense, measure of dependence.