Analysis of the performance of test statistics for detection of outliers (additive, innovative, transient, and level shift) in AR (1) processes.

Outlier detection has always been of interest for researchers and data miners. It has been well researched in different knowledge and application domains. This study aims at exploring the correctly identifying outliers using most commonly applied statistics. We evaluate the performance of AO, IO, LS, and TC as vulnerability to spurious outliers by means of empirical level of significance (ELS), power of the test indicating the sensitivity of the statistical tests in detecting changes and the vulnerability to masking of outliers in terms of misspecification frequencies are determined. We have observed that the sampling distribution of test statistic ηtp;tp=AO, IO, LS, TC  in case of AR(1) model is connected with the values of n and φ. The sampling distribution of  ηTC  is less concentrated than the sampling distribution of ηAO, ηIO, and ηLS. In AR(1) process, empirical critical values for 1%, 5%, and 10% upper percentiles are found to be higher than those generally used. We have also found the evidence that the test statistics for transient change (TC) needs to be revisited as the test statistics ηTCis found to be eclipsed by ηAO, ηLSand ηIOat different δ values. TC keeps on confusing with IO and AO, and at extreme δ values it just gets equal to AO and LS. [ABSTRACT FROM PUBLISHER]