Preprint Article Version 3 Preserved in Portico This version is not peer-reviewed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)

How to cite: Çankaya, M.N. Propositions for Confidence Interval in Systematic Sampling on Real Line. Preprints 2016, 2016080017. https://doi.org/10.20944/preprints201608.0017.v3 Çankaya, M.N. Propositions for Confidence Interval in Systematic Sampling on Real Line. Preprints 2016, 2016080017. https://doi.org/10.20944/preprints201608.0017.v3

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

Comments (0)

We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.

Leave a public comment
Send a private comment to the author(s)
* All users must log in before leaving a comment
Views 0
Downloads 0
Comments 0
Metrics 0


×
Alerts
Notify me about updates to this article or when a peer-reviewed version is published.
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here.