Abstract
Order preserving estimation is an estimation method that can retain the original order of the population parameters of interest. It is an important tool in many applications such as data visualization. In this paper, we focus on the population mean as our primary estimation function, and propose effective query processing strategy that can preserve the estimated order to be correct with probabilistic guarantees. We define the cost function as the number of samples taken for all the groups, and our goal is to make the sample size as small as possible. We compare our methods with state-of-the-art near-optimal algorithm in the literature, and achieve up to \(80\,\%\) reduction in the total sample size.
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- 1.
The order is induced from progressive sampling without replacement process. I.e., \(Rank(X_i) < Rank(X_j)\), if \(i < j\).
- 2.
Same as before we use \(g_i\) as the group with rank i in a candidate order in the rest of Sect. 4 in order to keep the notation clean.
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Chen, C., Wang, W., Wang, X., Yang, S. (2016). Effective Order Preserving Estimation Method. In: Cheema, M., Zhang, W., Chang, L. (eds) Databases Theory and Applications. ADC 2016. Lecture Notes in Computer Science(), vol 9877. Springer, Cham. https://doi.org/10.1007/978-3-319-46922-5_29
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DOI: https://doi.org/10.1007/978-3-319-46922-5_29
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