NDLI: An application of robust ridge regression model in the presence of outliers to real data problem

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An application of robust ridge regression model in the presence of outliers to real data problem

Content Provider	Scilit
Author	Shariff, N. S. Ferdaos, N. A.
Copyright Year	2017
Description	Journal: Journal of Physics: Conference Series Multicollinearity and outliers are often leads to inconsistent and unreliable parameter estimates in regression analysis. The well-known procedure that is robust to multicollinearity problem is the ridge regression method. This method however is believed are affected by the presence of outlier. The combination of GM-estimation and ridge parameter that is robust towards both problems is on interest in this study. As such, both techniques are employed to investigate the relationship between stock market price and macroeconomic variables in Malaysia due to curiosity of involving the multicollinearity and outlier problem in the data set. There are four macroeconomic factors selected for this study which are Consumer Price Index (CPI), Gross Domestic Product (GDP), Base Lending Rate (BLR) and Money Supply (M1). The results demonstrate that the proposed procedure is able to produce reliable results towards the presence of multicollinearity and outliers in the real data.
Related Links	http://iopscience.iop.org/article/10.1088/1742-6596/890/1/012150/pdf
ISSN	17426588
e-ISSN	17426596
DOI	10.1088/1742-6596/890/1/012150
Journal	Journal of Physics: Conference Series
Issue Number	1
Volume Number	890
Language	English
Publisher	IOP Publishing
Publisher Date	2017-09-20
Access Restriction	Open
Subject Keyword	Journal: Journal of Physics: Conference Series Applied Mathematics Data Problem Problem Multicollinearity Ridge Regression Stock Market
Content Type	Text
Resource Type	Article
Subject	Physics and Astronomy

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