Modeling Medical Data Using MM-Estimation Applied to Body Mass Index Data.

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dc.contributor.authorNor Azlida Aleng
dc.date.accessioned2017-04-02T02:13:10Z
dc.date.available2017-04-02T02:13:10Z
dc.date.issued2015
dc.description.abstractIn 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 modelen_US
dc.identifier.issn1662-7482
dc.identifier.urihttp://hdl.handle.net/123456789/5153
dc.language.isoenen_US
dc.publisherApplied Mathematical Sciencesen_US
dc.subjectNyi Nyi Naingen_US
dc.subjectZurkurnai Yusofen_US
dc.subjectNorizan Mohameden_US
dc.subjectRobust regressionen_US
dc.subjectMM-estimationen_US
dc.subjectOutliersen_US
dc.titleModeling Medical Data Using MM-Estimation Applied to Body Mass Index Data.en_US
dc.typeArticleen_US
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