Entropy based dependence measures are used as an alternative to correlation for determining the lag dependency of time series models. In this study, we explore the properties of partial autoinformation function (PAIF) to identify the lag dependency of non-linear and non-Gaussian autoregressive models. Non-parametric estimators of autoinformation function (AIF) and PAIF are obtained and then studied its asymptotic properties. A bootstrap algorithm is developed for testing significance of PAIF at different lags. Finally, we present numerical study to illustrate the use of AIF and PAIF for identifying the order of AR processes.

中文翻译：

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为非高斯时间序列建模检测条件独立性

基于熵的相关性度量用作确定时间序列模型的滞后相关性的相关性的替代方法。在这项研究中，我们探索了部分自动信息函数（PAIF）的特性，以识别非线性和非高斯自回归模型的滞后依赖性。获得自动信息函数（AIF）和PAIF的非参数估计量，然后研究其渐近性质。开发了一种自举算法来测试不同时滞下PAIF的重要性。最后，我们提供了数值研究来说明使用AIF和PAIF来确定AR过程的顺序。