Mengatasi Bias pada Penduga Parameter Metode Kuadrat Terkecil dalam Analisis Regresi Linear Sederhana dengan Bootstrap Data Berpasangan

Abstract

The assumption of normality cannot be fulfilled in regression analysis if there are outliers in observation data so that peduga parameters using LSM (Least Square Method) will produce biased estimators and not BLUE (Best Linear Unbiased Estimator). Based on this, the bootstrap method which is a repeat sampling method (resampling) that does not require distribution assumptions on the data can be used. In this study, paired bootstrap data method is used for simulation data with the number of outliers of 5%, 10% and 15%. This is to show how the influence of outliers on the distribution of data if the outliers given have different amounts. After estimating the parameters using paired bootstrap data, parameter estimator values and the resulting bias are not much different from the MKT parameter estimator values before being given an outlier. In this study at a 99% confidence interval for the case of simulation data with 10% and 15% of the parameter estimator outliers obtained in the previous study and in this study no longer produce BLUE estimators as in the simulation data with 5% outliers. [OVERCOMING BIAS IN PARAMETER ESTIMATORS OF THE LEAST SQUARE METHOD IN A SIMPLE LINEAR REGRESSION ANALYSIS WITH PAIRED DATA BOOTSTRAP] (J. Sains Indon., 42(2): 31-37, 2018)

Keywords:
Least Square Method, Best Linear Unbiased Estimator, Paired Bootstrap