Novel Methods For Estimation Of Population Mean Using Auxiliary Information Under PPS Sampling: Application With Real And Simulated Data Sets

Abstract

The use of simple random sampling (SRS) ensures that each and every unit in the population has an equal chance of being included in the sample. Due to the fact that it disregards the potential significance of the units size, SRS does not appear to be an appropriate strategy on the other hand when the units differ greatly in size. In these kinds of circumstances, it is possible that selecting units with the help of unequal probabilities instead of sampling with equal probability will result in more accurate estimations. In this method, the units are selected with a probability proportional to size (PPS) sampling that corresponds to a predetermined size measure. In order to estimate the finite population mean, this paper proposed a logarithmic-type estimator that relies on PPS sampling and simple random sampling. If the sampling technique is applied symmetrically across the population, biases can be minimized and the sample can be more accurately used to represent the entire population. We conducted an extensive numerical analysis and simulation study to evaluate the proposed estimator. We also provide visual representations of the results to show how well the proposed estimator works. In light of the numerical outcome, we see that the proposed estimator is more useful for estimating the population means using PPS sampling methods.