Abstract
Post-stratification is used when the size of each stratum is known, but frame of each stratum is unknown. Assumed strata contain clusters of unequal size; a random sample of some clusters is drawn and post-stratified according to the existing stratum of the population. This paper considers the mean estimation problem under the post-stratified cluster sampling setup. A modified weight structure is proposed to combine different cluster means. Attempt is made to obtain the optimum variance and estimate of the variance. The efficiency comparison of estimator is numerically supported by database study. All clustering-related results were obtained from Web Accessible Genome cluster open dataset. Mean estimation under post-stratified cluster sampling scheme shows overall accuracy of 92%.
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Raja Sekar, M., Sandhya, N. (2019). Mean Estimation Under Post-stratified Cluster Sampling Scheme. In: Bapi, R., Rao, K., Prasad, M. (eds) First International Conference on Artificial Intelligence and Cognitive Computing . Advances in Intelligent Systems and Computing, vol 815. Springer, Singapore. https://doi.org/10.1007/978-981-13-1580-0_27
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DOI: https://doi.org/10.1007/978-981-13-1580-0_27
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