Preprint Article Version 3 NOT YET PEER-REVIEWED

Propositions for Confidence Interval in Systematic Sampling on Real Line

  1. Department of Statistics, Faculty of Arts and Science, University of Uşak, Ankara-İzmir Karayolu 8.Km.1.Eylül Kampüsü UŞAK - 64200, Turkey
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. Propositions for Confidence Interval in Systematic Sampling on Real Line. Preprints 2016, 2016080017 (doi: 10.20944/preprints201608.0017.v3). Çankaya, M. Propositions for Confidence Interval in Systematic Sampling on Real Line. Preprints 2016, 2016080017 (doi: 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.

Subject Areas

biomedical imaging; covariogram; design-based stereology; estimation of volume; systematic sampling

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