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Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Nor Azlida Aleng | - |
dc.date.accessioned | 2017-04-02T02:13:10Z | - |
dc.date.available | 2017-04-02T02:13:10Z | - |
dc.date.issued | 2015 | - |
dc.identifier.issn | 1662-7482 | - |
dc.identifier.uri | http://hdl.handle.net/123456789/5153 | - |
dc.description.abstract | In medical statistics research, there are many methodologies used to investigate and to model the relationship between two or more variables. A model is often not useful when its fails to fit the data and the outliers may exist. Outliers play important role in regression. An outliers (observations) that is quite different from most the other values or observations in a data set. Robust regression is the most popular method that has been used to detect outliers and to provide resistant results in the presence of outliers in the data set. The purpose of this study is to show that, robust MM-estimation is an alternative approach in dealing with outliers presence in the medical data. This approach is extremely useful in identifying outliers and assessing the adequacy of a fitted model | en_US |
dc.language.iso | en | en_US |
dc.publisher | Applied Mathematical Sciences | en_US |
dc.subject | Nyi Nyi Naing | en_US |
dc.subject | Zurkurnai Yusof | en_US |
dc.subject | Norizan Mohamed | en_US |
dc.subject | Robust regression | en_US |
dc.subject | MM-estimation | en_US |
dc.subject | Outliers | en_US |
dc.title | Modeling Medical Data Using MM-Estimation Applied to Body Mass Index Data. | en_US |
dc.type | Article | en_US |
Appears in Collections: | Journal Articles |
Files in This Item:
File | Description | Size | Format | |
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35-Modeling Medical Data Using MM-Estimation Applied to Body Mass Index Data. .pdf | 344.86 kB | Adobe PDF | View/Open |
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