Modifiye sıralı küme örneklemesi yöntemlerinde regresyon kestiricilerinin etkinliklerinin incelenmesi

Ranked Set Sampling (RSS) has been a popular sampling method in recent years. RSS is used when visual ranking can be done easily while the variable of interest is difficult or expensive to measure. In the last years, different modifications of the RSS which are Pair RSS, Extreme RSS, Median RSS, Double RSS, LRSS and Truncation Based RSS methods have been used in wide applications and suggested by many researchers. The aim of this study is to estimate the regression estimators and to compare relative efficiencies of mean square errors of the regression models, population mean and regression coefficients for Simple Random Sampling (SRS), RSS and the different modified RSS methods. The Monte Carlo simulation study is performed via R Project with 10,000 repetitions. The performances of the estimators are compared based on bias, mean square error and relative efficiency for different levels of correlation coefficient, set and cycle sizes under Bivariate Normal Distribution (BVN) with different parameters for normal and outlier cases. The results indicate that the regression estimators under the modified RSS methods performs better than the regression estimators under SRS.