A mathematical method based on a nearest neighbor spatial Poisson process is described for assessing stochastic randomness in three-dimensional Euclidean space. The classical central limit theorem is invoked to obtain a normal approximation formula for testing the hypothesis of randomness. The performance of the method is evaluated with Monte Carlo simulations. A brief description is given of the software employed for implementation of the method in practice.
Get full access to this article
View all access options for this article.
References
1.
FellerW. (1950) An introduction to probability theory and its applications. Vols. I/II. New York: Wiley.
2.
HoelP. G.PortS. C., & StoneC. J. (1971a) Introduction to probability theory. Boston, MA: Houghton Mifflin.
3.
HoelP. G.PortS. C., & StoneC. J. (1971b) Introduction to statistical theory. Boston, MA: Houghton Mifflin.
4.
HoelP. G.PortS. C., & StoneC. J. (1972) Introduction to stochastic processes. Prospect Heights, IL: Waveland Press.
5.
O'BrienF. (1994a) Probability methods for detecting randomness in small sample spatial distributions. Perceptual and Motor Skills, 78, 715–720.
6.
O'BrienF. (1994b) A test of randomness for finite spatial distributions. Perceptual and Motor Skills, 78, 707–714.
7.
O'BrienF. (1995) A comparison of two tests of planar randomness. Perceptual and Motor Skills, 80, 144–146.
8.
O'BrienF.NguyenC. T., & HammelS. E. (1994) Signal characterization applications of Poisson point processes in the ocean environment. (NUWC-NPT Technical Report 10, 412) Newport, RI: Naval Undersea Warfare Center Division.