Chain Ratio and Product Cum Generalized Product-Based Estimators for Population Mode using Two Auxiliary Information under Simple Random Sampling
Abstract
Statistics practitioners frequently come across datasets with skewed distributions. In this case, the mode is thought to be a better location-based than the mean or median. In this study a chain ratio cum generalized product and chain product cum generalized product type estimators are proposed for estimating the population mode of the study variable using information on two auxiliary variables under simple random sampling. The biases and mean square errors are mathematically determined to approximately order one, and theoretical comparisons with competing estimators are conducted. The present study demonstrates that the suggested estimators for simple random sampling have a lower mean square error than that of the simple mode estimator, and traditional ratio estimators under the obtained theoretical conditions. Our simulation results indicate that the suggested chain ratio cum generalized product estimator outperform than the existing estimators.
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