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Scanning Brownian Processes

Published online by Cambridge University Press:  01 July 2016

Robert J. Adler*
Affiliation:
Technion—Israel Institute of Technology and University of North Carolina
Ron Pyke*
Affiliation:
University of Washington
*
Postal address: Faculty of Industrial Engineering and Management, Technion—Israel Institute of Technology, Haifa, Israel 32000, and Department of Statistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-3260, USA. e-mail: adler@stat.unc.edu, robert@ieadler.technion.ac.il
∗∗ Postal address: Department of Mathematics, University of Washington, Seattle, WA 98195-4350, USA. e-mail: pyke@math.washington.edu

Abstract

The ‘scanning process' Z(t), t ∈ ℝk of the title is a Gaussian random field obtained by associating with Z(t) the value of a set-indexed Brownian motion on the translate t + A0 of some ‘scanning set' A0. We study the basic properties of the random field Z relating, for example, its continuity and other sample path properties to the geometrical properties of A0. We ask if the set A0 determines the scanning process, and investigate when, and how, it is possible to recover the structure of A0 from realisations of the sample paths of the random field Z.

Type
Stochastic Geometry and Statistical Applications
Copyright
Copyright © Applied Probability Trust 1997 

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Footnotes

Research supported in part by US–Israel Binational Science Foundation and Office of Naval Research.

Research supported in part by US–Israel Binational Science Foundation and NSF.

References

Adler, R. J. (1981) The Geometry of Random Fields. Wiley, London.Google Scholar
Adler, R. J. (1990) An Introduction to Continuity, Extrema, and Related Topics for General Gaussian Processes. (IMS Lecture Notes-Monograph Series 12).Google Scholar
Adler, R. J. and Pyke, R. (1991) Problem 91-3. IMS Bulletin 20, 407, 564.Google Scholar
Adler, R. J. and Pyke, R. (1993) Uniform quadratic variation for Gaussian processes. Stoch. Proc. Appl. 48, 191210.Google Scholar
Akutowicz, E. (1956) On the determination of the phase of the Fourier integral, I. Trans. Amer. Math. Soc. 83, 179192.Google Scholar
Akutowicz, E. (1957) On the determination of the phase of the Fourier integral, II. Trans. Amer. Math. Soc. 84, 237238.Google Scholar
Apostol, T. (1957) Mathematical Analysis. Addison-Wesley, Reading, MA.Google Scholar
Baxter, G. (1956) A strong limit theorem for Gaussian processes. Proc. Amer. Math. Soc. 7, 522527.Google Scholar
Beran, R. J. and Millar, P. W. (1986) Confidence sets for a multivariate distribution. Ann. Statist. 14, 431443.Google Scholar
Cramer, H. and Wold, H. (1936) Some theorems on distribution functions. J. London Math. Soc. 11, 290295.Google Scholar
Dudley, R. M. (1984) A course on empirical processes (école d'été de Probabilités de Saint-Flour XII-1982). Lecture Notes in Mathematics 1097. Springer, Berlin.Google Scholar
Garsia, A. M., Rodemich, E. and Rumsey, H. (1970) A real variable lemma and the continuity of paths of some Gaussian processes. Indiana Univ. Math. J. 20, 565578.Google Scholar
Kallay, M. (1975) The extreme bodies in the set of plane convex bodies with a given width function. Israel J. Math. 22, 203207.Google Scholar
Kolmogorov, A. N. (1933) Sulla determinazione empirica di une legge di distribuzione. Giornale Inst. Ital. Attuari. 4, 8391.Google Scholar
Klein, R. and Giné, E. (1975) On quadratic variation of processes with Gaussian increments. Ann. Prob. 3, 716721.Google Scholar
Mallows, C. L. and Clark, J. M. (1970) Linear intercept distributions do not characterize plane sets. J. Appl. Prob. 7, 873879.Google Scholar
Matheron, G. (1975) Random Sets and Integral Geometry. Wiley, New York.Google Scholar
Millane, R. P. (1990) Phase retrieval in crystallography and optics. J. Opt. Soc. Amer. 7, 394409.CrossRefGoogle Scholar
Nagel, W. (1993) Orientation-dependent chord length distributions characterize polygons. J. Appl. Prob. 30, 730736.Google Scholar
Pollard, D. (1984) Convergence of Stochastic Processes. Springer, New York.Google Scholar
Pyke, R. (1984) Asymptotic results for empirical and partial sum processes: a review. Can. J. Statist. 12, 241264.Google Scholar
Pyke, R. and Wilbour, D. C. (1988) New approaches for goodness-of-fit tests for multidimensional data. In Statistical Theory and Data Analysis II. ed. Matusita, K. Elsevier, Amsterdam. pp. 139154.Google Scholar
Rudin, W. (1987) Real and Complex Analysis. McGraw Hill, New York.Google Scholar
Sapagov, N. A. (1974) A uniquness problem for finite measures in Euclidean spaces. Problems in the theory of probability distributions. Zap. Nauk. Sem. Leningrad Otdel. Map. Inst. Steklov, 41, 313.Google Scholar
Schneider, R. (1993) Convex Bodies: The Brunn-Minkowski Theory. Cambridge University Press, Cambridge.Google Scholar
Walsh, J. B. (1982) Propagation of singularities in the Brownian sheet. Ann. Prob. 10, 279288.Google Scholar
Wiener, N. (1938) The homogeneous chaos. Amer. J. Math. 60, 897936.Google Scholar