Detecting Conditional Independence for Modeling Non-Gaussian Time Series

被引：0

作者：

Sudheesh K. Kattumannil

Deemat C. Mathew

G. Hareesh

机构：

[1] Indian Statistical Institute,

[2] St. Thomas College,undefined

[3] Naval Physical and Oceanographic Laboratory,undefined

来源：

Journal of the Korean Statistical Society | 2020年 / 49卷

关键词：

Autoinformation; AR processes; Bootstrap; Entropy; Lag dependence; Kernel estimation;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

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.

引用

页码：578 / 595

页数：17

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