Regression Estimator for Adaptive Cluster Sample
Chang-Kyoon Son

Chang-Kyoon Son, Department of Applied Statistics, Dongguk University, Dongdae,Gyeongju, Gyeongbuk, Korea, East Asian.

Manuscript received on 08 June 2019 | Revised Manuscript received on 14 June 2019 | Manuscript Published on 22 June 2019 | PP: 56-60 | Volume-8 Issue-8S2 June 2019 | Retrieval Number: H10110688S219/19©BEIESP

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Abstract: Background/Objectives: Adaptive cluster sampling (ACS) is known as a sampling design for rare and clustered objects. We suggest the regression estimator to improve the efficiency of ACS estimator. Methods/Statistical analysis: We estimate the population total of Pedicularisishidoyana Koidz. &Ohwi in the Gyeongju National Park by using the regression estimation for adaptive cluster sampling. We can consider an auxiliary variable which has strong correlation in the estimation procedure. To do this we simulate auxiliary variable has sample correlation r=0.86. The efficiency of the proposed estimator is evaluated by comparing the relative efficiency and 95% confidence limit of estimator with the typical adaptive cluster estimator.

Keywords: In This Study, we Found That The Regression Estimator
Scope of the Article: IoT Applied for Digital Content