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Empirical Likelihood for Outlier Detection and Estimation in Autoregressive Time Series
Author(s) -
Baragona Roberto,
Battaglia Francesco,
Cucina Domenico
Publication year - 2016
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12145
Subject(s) - outlier , autoregressive model , empirical likelihood , series (stratigraphy) , mathematics , anomaly detection , econometrics , time series , gaussian , statistics , identification (biology) , star model , autoregressive–moving average model , autoregressive integrated moving average , setar , computer science , data mining , estimator , paleontology , physics , botany , quantum mechanics , biology
Identification and estimation of outliers in time series is proposed by using empirical likelihood methods. Theory and applications are developed for stationary autoregressive models with outliers distinguished in the usual additive and innovation types. Some other useful outlier types are considered as well. A simulation experiment is used for studying the behaviour of the empirical likelihood‐based method in finite samples and indicates that the proposed methods are preferable when dealing with the non‐Gaussian data. Our simulations suggest that the usual sequential procedure for multiple outlier detection is suitable also for the methods based on empirical likelihood.