Article
Version 4
Preserved in Portico This version is not peer-reviewed
Propositions for Confidence Interval in Systematic Sampling on Real Line
Version 1
: Received: 2 August 2016 / Approved: 2 August 2016 / Online: 2 August 2016 (11:07:53 CEST)
Version 2 : Received: 16 August 2016 / Approved: 16 August 2016 / Online: 16 August 2016 (11:39:57 CEST)
Version 3 : Received: 9 September 2016 / Approved: 9 September 2016 / Online: 9 September 2016 (11:52:36 CEST)
Version 4 : Received: 14 September 2016 / Approved: 15 September 2016 / Online: 15 September 2016 (05:18:42 CEST)
Version 2 : Received: 16 August 2016 / Approved: 16 August 2016 / Online: 16 August 2016 (11:39:57 CEST)
Version 3 : Received: 9 September 2016 / Approved: 9 September 2016 / Online: 9 September 2016 (11:52:36 CEST)
Version 4 : Received: 14 September 2016 / Approved: 15 September 2016 / Online: 15 September 2016 (05:18:42 CEST)
A peer-reviewed article of this Preprint also exists.
Çankaya, M. Propositions for Confidence Interval in Systematic Sampling on Real Line. Entropy 2016, 18, 352, doi:10.3390/e18100352. Çankaya, M. Propositions for Confidence Interval in Systematic Sampling on Real Line. Entropy 2016, 18, 352, doi:10.3390/e18100352.
Abstract
The systematic sampling is used as a method to get the quantitative results from the tissues and the radiological images. Systematic sampling on real line (R) is a very attractive method within which the biomedical imaging is consulted by the practitioners. For the systematic sampling on R, the measurement function (MF) is occurred by slicing the three-dimensional object equidistant systematically. The currently used covariogram model in variance approximation proposed by [28,29] is tested for the different measurement functions in a class to see the performance on the variance estimation of systematically sampled R. This study is an extension of [17], and an exact calculation method is proposed to calculate the constant λ(q,N) of confidence interval in the systematic sampling. The exact value of constant λ(q,N) is examined for the different measurement functions as well. As a result, it is observed from the simulation that the proposed MF should be used to check the performances of the variance approximation and the constant λ(q,N). Synthetic data can support the results of real data.
Keywords
biomedical imaging; covariogram; design-based stereology; estimation of volume; systematic sampling
Subject
Computer Science and Mathematics, Mathematics
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment