Ratio estimators in adaptive cluster sampling

Arthur L. Dryver, Chang Tai Chao

研究成果: Article同行評審

41 引文 斯高帕斯(Scopus)


In most surveys data are collected on many items rather than just the one variable of primary interest. Making the most use of the information collected is a issue of both practical and theoretical interest. Ratio estimates for the population mean or total are often more efficient. Unfortunately, ratio estimation is straightforward with simple random sampling, but this is often not the case when more complicated sampling designs are used, such as adaptive cluster sampling. A serious concern with ratio estimates introduced with many complicated designs is lack of independence, a necessary assumption. In this article, we propose two new ratio estimators under adaptive cluster sampling, one of which is unbiased for adaptive cluster sampling designs. The efficiencies of the new estimators to existing unbiased estimators, which do not utilize the auxiliary information, for adaptive cluster sampling and the conventional ratio estimation under simple random sampling without replacement are compared in this article. Related result shows the proposed estimators can be considered as a robust alternative of the conventional ratio estimator, especially when the correlation between the variable of interest and the auxiliary variable is not high enough for the conventional ratio estimator to have satisfactory performance.

頁(從 - 到)607-620
出版狀態Published - 2007 9月

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 生態建模


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