Robust Estimation Methods And Outlier Detection In Mediation Models
Mediation models refer to the relationships among three variables: an independent variables (IV), a potential mediating variable (M), and a dependent variable (DV). When the relationship between the dependent variable (DV) and an independent variables (IV) can be accounted for by an intermediate...
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Main Author: | |
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Format: | Thesis |
Language: | English English |
Published: |
2010
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Online Access: | http://psasir.upm.edu.my/id/eprint/12439/1/FS_2010_24A.pdf http://psasir.upm.edu.my/id/eprint/12439/ |
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Summary: | Mediation models refer to the relationships among three variables: an independent
variables (IV), a potential mediating variable (M), and a dependent variable (DV).
When the relationship between the dependent variable (DV) and an independent
variables (IV) can be accounted for by an intermediate variable M, mediation is
said to occur. Simple mediation model consists of three regression equations.
The Ordinary Least Squares (OLS) method is often use to estimate the parameters
of the mediation model. However, due to the fact that outliers have an unduly
effect on the OLS estimates, we propose to incorporate robust M and MM
estimator which are not easily affected by outliers, in the estimation of the
mediation model which is called RobSim1 and RobSim2, respectively. The
numerical example indicates that various types of contamination in the simulated
data have arbitrarily large effect on the OLS estimates and the Sobel test. The
MM-estimator incorporated in RobSim2 has improved the precision of the
indirect effect of mediation model. The overall analysis clearly shows that the Simple Mediation Model based on RobSim2 is prominently the most excellent
result, because it is able to withstand various contamination in the m , x , and y -
axes (direction).
There is also concern not only when the data contain observations that are extreme
in the response variable but also in the regressor space, namely the leverage
points. A new measure for the identification of high-leverage point is called
Diagnostic Robust Generalized Potentials (DRGP) which is proposed previously.
The DRGP procedures incorporated the Robust Mahanalobis Distance (RMD)
based on the minimum volume ellipsoid (MVE) for identifying the set of cases
‘remaining’ (R) and a set of cases ‘deleted’(D), and then diagnostic approach is
used to confirm the suspected values. The DRGP procedure uses MAD as its cutoff
points. We suggest an alternative method for identification of high leverage
points in the mediation model. A modification is made to the DRGP procedure.
It was verified that both MAD and n Q have the same breakdown point that is
50%. Nonetheless, the efficiency of the n Q is higher (86%) than the MAD
(37%). This work inspired us to incorporate the n Q instead of the MAD in the
proposed algorithm. We refer the above new method of identifying potential
outliers in mediation analysis as ModDRGP1 where the MAD is incorporated in
the second step of the ModDRGP1 algorithm. In this thesis we also propose
another DRGP, which has modified step 2 and step 4 for identifying potential
outliers in mediation model. We called the second proposed method as
ModDRGP2. |
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