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Báo cáo vật lý: "AN OVERVIEW OF BIASED ESTIMATOR"

Tuyển tập các báo cáo nghiên cứu khoa học trên tạp chí khoa học vật lý quốc tế đề tài: AN OVERVIEW OF BIASED ESTIMATOR | Journal of Physical Science Vol. 18 2 89-106 2007 89 AN OVERVIEW OF BIASED ESTIMATORS Ng Set Foong1 Low Heng Chin2 and Quah Soon Hoe2 department of Information Technology and Quantitative Sciences Universiti Teknologi MARA Jalan Permatang Pauh 13500 Permatang Pauh Pulau Pinang Malaysia 2School of Mathematical Sciences Universiti Sains Malaysia 11800 USM Pulau Pinang Malaysia Corresponding author hclow@cs.usm.my shquah@gmail.com Abstrak Penganggar pincang telah dicadangkan sebagai satu cara untuk meningkatkan kejituan anggaran parameter dalam model regresi apabila kekolinearan wujud dalam model tersebut. Sebab-sebab untuk menggunakan penganggar pincang telah dibincangkan dalam kertas kerja ini. Satu senarai penganggar-penganggar pincang juga dirumuskan dalam kertas kerja ini. Abstract Some biased estimators have been suggested as a means of improving the accuracy of parameter estimates in a regression model when multicollinearity exists. The rationale for using biased estimators instead of unbiased estimators when multicollinearity exists is given in this paper. A summary for a list of biased estimators is also given in this paper. Keywords multicollinearity regression unbiased estimor 1. INTRODUCTION When serious multicollinearity is detected in the data some corrective actions should be taken in order to reduce its impact. The remedies for the problem of multicollinearity depend on the objective of the regression analysis. Multicollinearity causes no serious problem if the objective is to predict. However multicollinearity is a problem when our primary interest is in the estimation of parameters.1 The variances of parameter estimate when multicollinearity exists can become very large. Hence the accuracy of the parameter estimate is reduced. One obvious solution is to eliminate the regressors that are causing the multicollinearity. However selecting regressors to delete for the purpose of removing or reducing multicollinearity is not a safe strategy. Even with .