THE PERFORMANCE OF LEVERAGE BASED NEAR NEIGHBOUR-ROBUST WEIGHT LEAST SQUARES IN MULTIPLE LINEAR REGRESSION IN THE PRESENCE OF HETEROSCEDASTIC ERRORS AND OUTLIER

Authors

  • Khoo Li Peng Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia, 81310, UTM Johor Bahru, Johor Darul Ta’azim, Malaysia
  • Robiah Adnan Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia, 81310, UTM Johor Bahru, Johor Darul Ta’azim, Malaysia
  • Maizah Hura Ahmad Department of Mathematical Science, Faculty of Science, Universiti Teknologi Malaysia, 81310, UTM Johor Bahru, Johor Darul Ta’azim, Malaysia

DOI:

https://doi.org/10.11113/jt.v76.5820

Keywords:

Heteroscedastic errors, outliers, Leverage Based Near Neighbour–Robust Weighted Least Squares, Monte Carlo simulation, standard errors

Abstract

In this study, Leverage Based Near Neighbour–Robust Weighted Least Squares (LBNN-RWLS) method is proposed in order to estimate the standard error accurately in the presence of heteroscedastic errors and outliers in multiple linear regression. The data sets used in this study are simulated through monte carlo simulation. The data sets contain heteroscedastic errors and different percentages of outliers with different sample sizes.  The study discovered that LBNN-RWLS is able to produce smaller standard errors compared to Ordinary Least Squares (OLS), Least Trimmed of Squares (LTS) and Weighted Least Squares (WLS). This shows that LBNN-RWLS can estimate the standard error accurately even when heteroscedastic errors and outliers are present in the data sets.

References

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Habshah, M., Rana M. S., Imon, A. H. M. R. 2009. The Performance of Robust Weighted Least Squares in the Presence of Outliers and Heteroscedastic Errors. WSEAS Transactions on Mathematics. 7(8): 351-361.

Montgomery, D. G., Peck, D. E. and Vining, G. G. 2001. Introduction to Linear Regression Analysis. 3rd ed. John Wiley and Sons, New York.

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Issue

Vol. 76 No. 13: Steering Innovation, Serving Society in Achieving Global Excellence Towards Science and Technology

Section

Science and Engineering

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Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.

How to Cite

THE PERFORMANCE OF LEVERAGE BASED NEAR NEIGHBOUR-ROBUST WEIGHT LEAST SQUARES IN MULTIPLE LINEAR REGRESSION IN THE PRESENCE OF HETEROSCEDASTIC ERRORS AND OUTLIER. (2015). Jurnal Teknologi, 76(13). https://doi.org/10.11113/jt.v76.5820