Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-28T14:37:48.588Z Has data issue: false hasContentIssue false

On the estimation of a star-shaped set

Published online by Cambridge University Press:  01 July 2016

Amparo Baíllo*
Affiliation:
Universidad Autónoma de Madrid
Antonio Cuevas*
Affiliation:
Universidad Autónoma de Madrid
*
Postal address: Departamento de Matemáticas, Facultad de Ciencias, Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain.
Postal address: Departamento de Matemáticas, Facultad de Ciencias, Ciudad Universitaria de Cantoblanco, 28049 Madrid, Spain.

Abstract

The estimation of a star-shaped set S from a random sample of points X1,…,XnS is considered. We show that S can be consistently approximated (with respect to both the Hausdorff metric and the ‘distance in measure’ between sets) by an estimator ŝn defined as a union of balls centered at the sample points with a common radius which can be chosen in such a way that ŝn is also star-shaped. We also prove that, under some mild conditions, the topological boundary of the estimator ŝn converges, in the Hausdorff sense, to that of S; this has a particular interest when the proposed estimation problem is considered from the point of view of statistical image analysis.

Type
Stochastic Geometry and Statistical Applications
Copyright
Copyright © Applied Probability Trust 2001 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baíllo, A., Cuevas, A. and Justel, A. (2000). Set estimation and nonparametric detection. Canad. J. Statist. 28, 765782.Google Scholar
Bertholet, V., Rasson, J.-P. and Lissoir, S. (1998). About the automatic detection of training sets for multispectral images classification. In Advances in Data Science and Classification, eds Rizzi, A., Vichi, M. and Bock, H. H., Springer, Berlin, pp. 221226.Google Scholar
Breen, M. (1992). Illumination by translates of convex sets. Geom. Dedicata 42, 215222.Google Scholar
Chevalier, J. (1976). Estimation du support et du contour du support d'une loi de probabilité. Ann. Inst. H. Poincaré Prob. Statist. 12, 339364.Google Scholar
Cuevas, A. (1990). On pattern analysis in the non-convex case. Kybernetes 19, 2633.Google Scholar
Cuevas, A. and Fraiman, R. (1997). A plug-in approach to support estimation. Ann. Statist. 25, 23002312.Google Scholar
Cuevas, A. and Fraiman, R. (1998). On visual distances in density estimation: the Hausdorff choice. Statist. Prob. Lett. 40, 333341.CrossRefGoogle Scholar
Cuevas, A. and González-Manteiga, W. (1991). Data-driven smoothing based on convexity properties. In Nonparametric Functional Estimation and Related Topics, eds Roussas, G. G. et al., Kluwer, Dordrecht, pp. 225240.Google Scholar
Cuevas, A., Febrero, M. and Fraiman, R. (2000). Estimating the number of clusters. Canad. J. Statist. 28, 367382.CrossRefGoogle Scholar
Devroye, L. and Wise, G. L. (1980). Detection of abnormal behavior via nonparametric estimation of the support. SIAM J. Appl. Math. 38, 480488.Google Scholar
Dümbgen, L. and Walther, G. (1996). Rates of convergence for random approximations of convex sets. Adv. Appl. Prob. 28, 384393.Google Scholar
Gardner, R. J. (1995). Geometric Tomography. Cambridge University Press.Google Scholar
Grenander, U. (1956). On the theory of mortality measurement, II. Skand. Aktuarrietidskr. J. 39, 125153.Google Scholar
Grenander, U. (1981). Abstract Inference. John Wiley, New York.Google Scholar
Korostelev, A. P. and Tsybakov, A. B. (1993). Minimax Theory of Image Reconstruction. Springer, New York.CrossRefGoogle Scholar
Mammen, E., Marron, J. S. and Fisher, N. I. (1992). Some asymptotics for multimodality tests based on kernel density estimates. Prob. Theory Relat. Fields 91, 115132.Google Scholar
Molchanov, I. S. (1998). A limit theorem for solutions of inequalities. Scand. J. Statist. 25, 235242.Google Scholar
Moore, M. (1984). On the estimation of a convex set. Ann. Statist. 12, 10901099.CrossRefGoogle Scholar
Penrose, M. D. (1999). A strong law for the longest edge of the minimal spanning tree. Ann. Prob. 27, 246260.Google Scholar
Polonik, W. (1998). The silhouette, concentration functions and ML-density estimation under order restrictions. Ann. Statist. 26, 18571877.Google Scholar
Rényi, A. and Sulanke, R. (1963). Über die konvexe Hülle von n zufällig gewählten Punkten. Z. Wahrscheinlichkeitsth. 2, 7584.Google Scholar
Rényi, A. and Sulanke, R. (1964). Über die konvexe Hülle von n zufällig gewählten Punkten (II). Z. Wahrscheinlichkeitsth. 3, 138147.Google Scholar
Ripley, B. D. and Rasson, J. P. (1977). Finding the edge of a Poisson forest. J. Appl. Prob. 14, 483491.Google Scholar
Rudemo, M. and Stryhn, H. (1994a). Approximating the distribution of maximum likelihood contour estimators in two-region images. Scand. J. Statist. 21, 4155.Google Scholar
Rudemo, M. and Stryhn, H. (1994b). Boundary estimation for star-shaped objects. In Change-point Problems, eds Carlstein, E., Müller, H.-G. and Siegmund, D., Institute of Mathematical Statistics, Hayward, CA, pp. 276283.CrossRefGoogle Scholar
Schneider, R. (1988). Random approximation of convex sets. J. Microscopy 151, 211227.Google Scholar
Schneider, R. (1993). Convex Bodies: The Brunn–Minkowski Theory. Cambridge University Press.Google Scholar
Toranzos, F. A. (1967). Radial functions of convex and star-shaped bodies. Amer. Math. Monthly 74, 278280.Google Scholar
Tsybakov, A. B. (1997). On nonparametric estimation of density level sets. Ann. Statist. 25, 948969.Google Scholar
Walther, G. (1997). Granulometric smoothing. Ann. Statist. 25, 22732299.Google Scholar
Walther, G. (1999). On a generalization of Blaschke's rolling theorem and the smoothing of surfaces. Math. Meth. Appl. Sci. 22, 301316.3.0.CO;2-M>CrossRefGoogle Scholar
Wand, M.P. and Jones, M.C. (1995). Kernel Smoothing. Chapman and Hall, London.Google Scholar