TY - JFULL AU - Nuanpan Nangsue PY - 2009/6/ TI - Adjusted Ratio and Regression Type Estimators for Estimation of Population Mean when some Observations are missing T2 - International Journal of Mathematical and Computational Sciences SP - 333 EP - 337 VL - 3 SN - 1307-6892 UR - https://publications.waset.org/pdf/14354 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 29, 2009 N2 - Ratio and regression type estimators have been used by previous authors to estimate a population mean for the principal variable from samples in which both auxiliary x and principal y variable data are available. However, missing data are a common problem in statistical analyses with real data. Ratio and regression type estimators have also been used for imputing values of missing y data. In this paper, six new ratio and regression type estimators are proposed for imputing values for any missing y data and estimating a population mean for y from samples with missing x and/or y data. A simulation study has been conducted to compare the six ratio and regression type estimators with a previous estimator of Rueda. Two population sizes N = 1,000 and 5,000 have been considered with sample sizes of 10% and 30% and with correlation coefficients between population variables X and Y of 0.5 and 0.8. In the simulations, 10 and 40 percent of sample y values and 10 and 40 percent of sample x values were randomly designated as missing. The new ratio and regression type estimators give similar mean absolute percentage errors that are smaller than the Rueda estimator for all cases. The new estimators give a large reduction in errors for the case of 40% missing y values and sampling fraction of 30%. ER -