Statistical Shape Methodology for the Analysis of Helices

Mai F. Alfahad (), John T. Kent () and Kanti V. Mardia ()
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Mai F. Alfahad: University of Leeds
John T. Kent: University of Leeds
Kanti V. Mardia: University of Leeds

Sankhya A: The Indian Journal of Statistics, 2018, vol. 80, issue 1, No 2, 8-32

Abstract: Abstract Consider a helix in three-dimensional space along which a sequence of equally spaced points is observed, subject to statistical noise. For data coming from a single helix, a two-stage algorithm based on a profile likelihood is developed to compute the maximum likelihood estimate of the helix parameters. Statistical properties of the estimator are studied and comparisons are made to other estimators found in the literature. Next a likelihood ratio test is developed to test if there is a change point in the helix, splitting the data into two sub-helices. The shapes of protein α-helices are used to illustrate the methodology.

Keywords: Change point; Helix axis; Kinked helix; Principal component analysis; Procrustes analysis; Shape analysis; Primary 62H11; Secondary 62P10; 92C40. (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13171-018-0144-8

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