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Keywords
(12)
Comparative Study
Least Square Method
Least Squares Estimate
Linear Equations
Mean Square Error
Monte Carlo Simulation
Partial Least Square Regression
Principal Component Analysis
Principal Component Regression
Regression Analysis
Ridge Regression
Ordinary Least Squares Regression
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A Comparative Study On Some Methods For Handling Multicollinearity Problems
A Comparative Study On Some Methods For Handling Multicollinearity Problems,Norliza Adnan,Maizah Hura Ahmad,Robiah Adnan
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A Comparative Study On Some Methods For Handling Multicollinearity Problems
(
Citations: 2
)
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Norliza Adnan
,
Maizah Hura Ahmad
,
Robiah Adnan
In regression, the objective is to explain the variation in one or more response variables, by associating this variation with proportional variation in one or more explanatory variables. A frequent obstacle is that several of the explanatory vari ables will vary in rather similar ways. As a result, their collective power of explanation is considerably less than the sum of their individual powers. This phenomenon called multicollinearity, is a common problem in regression analysis. Handling multicollinear ity problem in
regression analysis
is important because least squares estimations as sume that predictor variables are not correlated with each other. The performances of
ridge regression
(RR),
principal component regression
(PCR) and partial least squares regression (PLSR) in handling multicollinearity problem in simulated data sets are compared to help and give future researchers a comprehensive view about the best procedure to handle multicollinearity problems. PCR is a combination of
principal component analysis
(PCA) and
ordinary least squares regression
(OLS) while PLSR is an approach similar to PCR because a component that can be used to reduce the number of variables need to be constructed. RR on the other hand is the modified
least square method
that allows a biased but more precise estimator. The algorithm is described and for the purpose of comparing the three methods, simulated data sets where the number of cases were less than the number of observations used. The goal was to develop a linear equation that relates all the predictor variables to a response variable. For comparison purposes,
mean square
errors (MSE) were calculated. A
Monte Carlo simulation
study was used to evaluate the eectiveness of these three procedures. The analysis including all simulations and calculations were done using statistical package SPlus 2000 software.
Published in 2006.
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Citation Context
(1)
...Large dfs is one of the major issues with MLAS. To deal with this problem, we propose a PLSbased MLAS approach, while avoiding large dfs. Simulation study based on real data from the HapMap project suggests that our PLSbased MLAS generally outperformed other three popular MLAS approaches under various scenarios investigated in this study. PLS is suitable to handle the data with many independent variables as well as multicollinearity among the variables
...
Feng Zhang
,
et al.
Multilocus Association Testing of Quantitative Traits Based on Partial...
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Citations
(2)
Multilocus Association Testing of Quantitative Traits Based on Partial LeastSquares Analysis
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,
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,
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,
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