Identification Of Outliers In Time Series Data
In regression analysis, data sets usually contain unusual observations that produces undesirable effects on least squares estimates, this unusual observations are refer to as outliers. Detecting these unusual observations prior data analysis is an important aspect of model building. However, many...
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| اللغة: | en_US |
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| الملخص: | In regression analysis, data sets usually contain unusual observations that produces
undesirable effects on least squares estimates, this unusual observations are refer to as
outliers. Detecting these unusual observations prior data analysis is an important
aspect of model building. However, many regression diagnostics techniques have been
introduced to detect these outliers. This research compares the performance of five
regression diagnostics techniques based on Ordinary Least Square (OLS) estimators
namely; standardized residuals, studentized residuals, Hadi's influence measure,
Welsch Kuh distance and Cook's distance to detect and identify outliers. It is known
that OLS is not robust in the presence of multiple outliers and high leverage points.
Therefore, several robust regression models are used as alternative and its approach is
more reliable and appropriate method for solving this problem. The robust regressions
are M-estimation, Least Absolute Deviation (Ll), Least Median Square (LMS) and
Least Trimmed Square (LTS). The comparisons are made via simulation studies and
real data. This research also study the critical values of each techniques and our own
critical values are computed for this research. Our results have shown that in some
cases diagnostics based on OLS and some robust estimators give similar outcomes,
they detect the same percentage of correct outlier detection. The results also shows
that Least Trimmed Square is the best among all its counterparts followed by LMS, M
estimator and L1 perform least. |
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