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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Main Author:	Adewale Asiata Omotoyosi
Format:	Thesis
Language:	en_US
Subjects:	Regression analysis Outliers (Statistics)
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id	my-usim-ddms-12455
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spelling	my-usim-ddms-124552024-05-29T04:12:04Z Identification Of Outliers In Time Series Data Adewale Asiata Omotoyosi 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. Universiti Sains Islam Malaysia 2017-07 Thesis en_US https://oarep.usim.edu.my/handle/123456789/12455 https://oarep.usim.edu.my/bitstreams/8a637a74-3f7d-45f3-920b-ca3a6ea4cf36/download 8a4605be74aa9ea9d79846c1fba20a33 Regression analysis Outliers (Statistics)
institution	Universiti Sains Islam Malaysia
collection	USIM Institutional Repository
language	en_US
topic	Regression analysis Outliers (Statistics)
spellingShingle	Regression analysis Outliers (Statistics) Adewale Asiata Omotoyosi Identification Of Outliers In Time Series Data
description	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.
format	Thesis
author	Adewale Asiata Omotoyosi
author_facet	Adewale Asiata Omotoyosi
author_sort	Adewale Asiata Omotoyosi
title	Identification Of Outliers In Time Series Data
title_short	Identification Of Outliers In Time Series Data
title_full	Identification Of Outliers In Time Series Data
title_fullStr	Identification Of Outliers In Time Series Data
title_full_unstemmed	Identification Of Outliers In Time Series Data
title_sort	identification of outliers in time series data
granting_institution	Universiti Sains Islam Malaysia
_version_	1812444792753225728

Identification Of Outliers In Time Series Data

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