Outlier detection and parameter estimation in multivariate multiple regression (MMR)
Issued Date
2013
Issued Date (B.E.)
2556
Available Date
Copyright Date
Resource Type
Series
Edition
Language
eng
File Type
application/pdf
No. of Pages/File Size
117 leaves
ISBN
ISSN
eISSN
Other identifier(s)
b184491
Identifier(s)
Access Rights
Access Status
Rights
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Rights Holder(s)
Physical Location
National Institute of Development Administration. Library and Information Center
Bibliographic Citation
Citation
Paweena Tangjuang (2013). Outlier detection and parameter estimation in multivariate multiple regression (MMR). Retrieved from: http://repository.nida.ac.th/handle/662723737/3067.
Title
Outlier detection and parameter estimation in multivariate multiple regression (MMR)
Alternative Title(s)
Author(s)
Advisor(s)
Editor(s)
item.page.dc.contrubutor.advisor
Advisor's email
Contributor(s)
Contributor(s)
Abstract
Outlier detection in Y-direction for multivariate multiple regression data is
interesting since there are correlations between the dependent variables which is one
cause of difficulty in detecting multivariate outliers, furthermore, the presence of the
outliers may change the values of the estimators arbitrarily. Having an alternative
method that can detect those outliers is necessary so that reliable results can be
obtained. The multivariate outlier detection methods have been developed by many
researchers. But in this study, Mahalanobis Distance method, Minimum Covariance
Determinant method and Minimum Volume Ellipsoid method were considered and
compared to the proposed method which tried to solve outlier detection problem when
the data containing the correlated dependent variables and having very large sample
size. The proposed method was based on the squared distances of the residuals to find
the robust estimates of location and covariance matrix for calculating the robust
distances of Y. The behavior of the proposed method was evaluated through Monte
Carlo simulation studies. It was demonstrated that the proposed method could be an
alternative method used to detect those outliers for the cases of low, medium and high
correlations/variances of the dependent variables. Simulations with contaminated
datasets indicated that the proposed method could be applied efficiently in the case of
data having large sample sizes. That is, the principal advantage of the proposed
algorithm is to solve the complicated problem of resampling algorithm which occurs
when the sample size is large.
When data contain outliers, the ordinary least-squares estimator is no longer
appropriate. For obtaining the parameter estimates of data with outliers, we analyze
Multivariate Weighted Least Squares (MWLS) estimator. The estimates of the
regression coefficients using the proposed method were compared to those of using
MCD and MVE method. For comparing the properties of the estimation procedures,
we focus on the values of Bias and Mean Squared Error (MSE) of the estimated
coefficients. For most of the values of Bias and MSE in the case of large sample size,
the proposed method gave lower values of Bias and MSE than the others with any
percentages of Y-outliers.
Table of contents
Description
Dissertation (Ph.D. (Statistics))--National Institute of Development Administration, 2013.