ON THE PRECISION OF CURVE LENGTH ESTIMATION IN THE PLANE
DOI:
https://doi.org/10.5566/ias.1412Keywords:
Buffon-Steinhaus estimator, Cauchy estimator, curve length, error variance prediction, Monte Carlo resampling, test gridAbstract
The estimator of planar curve length based on intersection counting with a square grid, called the Buffon-Steinhaus estimator, is simple, design unbiased and efficient. However, the prediction of its error variance from a single grid superimposition is a non trivial problem. A previously published predictor is checked here by means of repeated Monte Carlo superimpositions of a curve onto a square grid, with isotropic uniform randomness relative to each other. Nine curvilinear features (namely flattened DNA molecule projections) were considered, and complete data are shown for two of them. Automatization required image processing to transform the original tiff image of each curve into a polygonal approximation consisting of between 180 and 416 straight line segments or ‘links’ for the different curves. The performance of the variance prediction formula proved to be satisfactory for practical use (at least for the curves studied).
References
Baddeley AJ, Jensen EBV (2005). Stereology for statisticians. London: Chapman &Hall/CRC.
Cochran W (1977). Sampling Techniques. 3rd Ed. New York: J. Wiley & Sons.
Cruz-Orive LM (1989). Precision of systematic sampling on a step function. In: Hubler A, Nagel W, Ripley B, Werner G, eds., Geobild’89, pp. 185–193. Berlin: Akademie Verlag.
Cruz-Orive LM, Gelsvartas J, Roberts N (2014). Sampling theory and automated simulations for vertical sections, applied to human brain. J Microsc 253:119–50.
Cruz-Orive LM, Gual-Arnau X (2002). Precision of circular systematic sampling. J Microsc 207:225–42.
Cruz-Orive LM, Insausti A, Insausti R, Crespo D (2004). A case study from Neuroscience involving Stereology and Multivariate Analysis. In: Evans S, Janson A, Nyengaard J, eds., Quantive Methods in Neuroscience, Ch. 2, pp. 16–64. Oxford: Practical Neuroscience Series, Oxford University Press, 16–64.
García-Fiñana M, Cruz-Orive LM (2004). Improved variance prediction for systematic sampling on R. Stat A J Theor Appl Stat 38:243–72.
Gundersen HJG (1977). Notes on the estimation of the numerical density of arbitrary profiles: the edge effect. J Microsc 111:219–23.
Gundersen HJG, Østerby R (1981). Optimizing sampling efficiency of stereological studies in biology: or ’do more less well!’. J Microsc 121:65–73.
Howard V, Reed MG (2005). Unbiased stereology: Three-dimensional measurement in microscopy, 2nd Ed. Oxford: Bios/ Taylor & Francis.
Kiêu K, Souchet S, Istas J(1999). Precision of systematic sampling and transitive methods. J Stat Plan Inference 77:263–79.
Miles R (1974). On the elimitation of edge effects in planar sampling. In: Kendal E, Harding D, eds., Stochastic Geometry: A tribute to the memory of Rollo Davidson, pp 228–247. London: John Wiley and Sons.
Miles R, Davy P (1976). Precise and general conditions for the validity of a comprehensive set of stereological fundamental formulae. J Microsc 107:211–26.
Moran PAP (1966). Measuring the length of a curve. Biometrika 53:359–64.
Podestà A, Imperadori L, Colnaghi W, Finzi L, Milani P, Dunlap D (2004). Atomic force microscopy study of DNA deposited on poly l-ornithine-coated mica. J Microsc 215:236–40.
Podestà A, Indrieri M, Brogioli D, Manning GS, Milani P, Guerra R, Finzi L, Dunlap D (2005). Positively charged surfaces increase the flexibility of DNA. Biophys J 89:2558–63.
Santalo ́ L (1976). Integral Geometry and Geometric Probability. Reading, MA: Addison- Wesley.
Steinhaus H (1930). Zur Praxis der Rektifikation und zum Längenbegriff. Berichte Sachsischen Akad Wiss Leipzig 82:120–30.
Weibel ER (1979). Stereological methods. Practical methods for biological morphometry, vol. 1. London: Academic Press.