Bernoulli

Lévy-based growth models

Kristjana Ýr Jónsdóttir, Jürgen Schmiegel, and Eva B. Vedel Jensen

Source: Bernoulli Volume 14, Number 1 (2008), 62-90.

Abstract

In the present paper, we give a condensed review, for the nonspecialist reader, of a new modelling framework for spatio-temporal processes, based on Lévy theory. We show the potential of the approach in stochastic geometry and spatial statistics by studying Lévy-based growth modelling of planar objects. The growth models considered are spatio-temporal stochastic processes on the circle. As a by product, flexible new models for space–time covariance functions on the circle are provided. An application of the Lévy-based growth models to tumour growth is discussed.

Keywords: growth models; Lévy basis; spatio-temporal modelling; tumour growth

Full-text: Access denied (no subscription detected)

We're sorry, but we are unable to provide you with the full text of this article because we are not able to identify you as a subscriber.
If you have a personal subscription to this journal, then please login. If you are already logged in, then you may need to update your profile to register your subscription. Read more about accessing full-text
Links and Identifiers

Permanent link to this document: http://projecteuclid.org/euclid.bj/1202492785
Digital Object Identifier: doi:10.3150/07-BEJ6130

References

Alt, W. (1999). Statistics and dynamics of cellular shape changes. In On Growth and Form: Spatio-temporal Pattern Formation in Biology (M.A.J. Chaplain, G.D. Singh and J.C. McLachlan, eds.) 287–307. Chichester: Wiley.
Barndorff-Nielsen, O. and Schmiegel, J. (2004). Lévy based spatial–temporal modelling, with applications to turbulence. Russian Math. Surveys 59 63–90.
Barndorff-Nielsen, O. and Thorbjørnsen, S. (2003). A connection between classical and free infinite divisability. Technical Report 2003-7, MaPhySto, Univ. Aarhus, Denmark.
Bramson, M. and Griffeath, D. (1981). On the Williams–Bjerknes tumour growth model. I. Ann. Probab. 9 173–185.
Brix, A. (1998). Spatial and spatio-temporal models for weed abundance. Ph.D. thesis, Royal Veterinary and Agricultural Univ., Copenhagen.
Brix, A. (1999). Generalized Gamma measures and shot-noise Cox processes. Adv. in Appl. Probab. 31 929–953.
Brix, A. and Chadoeuf, J. (2002). Spatio-temporal modelling of weeds by shot-noise G Cox processes. Biom. J. 44 83–99.
Brix, A. and Diggle, P.J. (2001). Spatiotemporal prediction for log-Gaussian Cox processes. J. Roy. Statist. Soc. Ser. B 63 823–841.
Brix, A. and Møller, J. (2001). Space–time multitype log Gaussian Cox processes with a view to modelling weeds. Scand. J. Statist. 28 471–488.
Brú, A., Pastor, J.M., Fernaud, I., Brú, I., Melle, S. and Berenguer, C. (1998). Super-rough dynamics on tumour growth. Phys. Rev. Lett. 81 4008–4011.
Calder, I. (1986). A stochastic model of rainfall interception. J. Hydrology 89 65–71.
Cantalapiedra, I., Lacasta, A., Auguet, C., Peñaranda, A. and Ramirez-Piscina, L. (2001). Pattern formation modelling of bacterial colonies. In Branching in Nature 359–364. EDP Sciences, Springer.
Cressie, N. (1991a). Modelling growth with random sets. In Spatial Statistics and Imaging (A. Possolo and C.A. Hayward, eds.) 31–45. IMS Lecture Notes Monogr. Ser. 20. IMS, Hayward, CA.
Cressie, N. (1991b). Statistics for Spatial Data. New York: Wiley.
Cressie, N. and Hulting, F. (1992). A spatial statistical analysis of tumor growth. J. Amer. Statist. Assoc. 87 272–283.
Cressie, N. and Laslett, G.M. (1987). Random set theory and problems of modelling. SIAM Rev. 29 557–574.
Deijfen, M. (2003). Asymptotic shape in a continuum growth model. Adv. in Appl. Probab. 35 303–318.
Delsanto, P., Romano, A., Scalerandi, M. and Pescarmona, G. (2000). Analysis of a “phase transition” from tumor growth to latency. Phys. Rev. E 62 2547–2554.
Durrett, R. and Liggett, T. (1981). The shape of the limit set in Richardson’s growth model. Ann. Probab. 9 186–193.
Feideropoulou, G. and Pesquet-Popescu, B. (2004). Stochastic modelling of the spatio-temporal wavelet coefficients and applications to quality enhancement and error concealment. EURASIP JASP 12 1931–1942.
Fewster, R. (2003). A spatiotemporal stochastic process model for species spread. Biometrics 59 640–649.
Gratzer, G., Canham, C., Dieckmann, U., Fischer, A., Iwasa, Y., Law, R., Lexer, M., Sandman, H., Spies, T., Splechtna, B. and Szwagrzyk, L. (2004). Spatio-temporal development of forests – current trends in field methods and models. Oikos 107 3–15.
Hellmund, G. (2005). Lévy driven Cox processes with a view to modelling tropical rain forests. Master thesis, Dept. Mathematical Sciences, Univ. Aarhus.
Hellmund, G., Prokešová, M. and Jensen, E.B.V. (2007). Spatial and spatio-temporal Lévy based Cox point processes. Submitted.
Hobolth, A. and Jensen, E. (2000). Modelling stochastic changes in curve shape, with an application to cancer diagnostics. Adv. in Appl. Probab. 32 344–362.
Hobolth, A., Pedersen, J. and Jensen, E.B.V. (2003). A continuous parametric shape model. Ann. Inst. Statist. Math. 55 227–242.
Jensen, E.B.V., Jónsdóttir, K.Y., Schmiegel, J. and Barndorff-Nielsen, O.E. (2006). Spatio-temporal modelling – with a view to biological growth. In Statistical Methods of Spatio-Temporal Systems (B. Finkenstadt and V. Isham, eds.) 45–73. Boca Raton: Chapman & Hall/CRC.
Jónsdóttir, K.Ý. and Jensen, E.B.V. (2005). Gaussian radial growth. Image Analysis Stereology 24 117–126.
Kallenberg, O. (1989). Random Measures, 4th ed. Berlin: Akademie Verlag.
Kansal, A.R., Torquato, S., Harsh, G.R., Chiocca, E.A. and Deisboeck, T.S. (2000). Simulated brain tumor growth dynamics using a three-dimensional cellular automaton. J. Theor. Biol. 203 367–382.
Kwapien, S. and Woyczynski, W. (1992). Random Series and Stochastic Integrals: Single and Multiple. Boston: Birkhäuser.
Lee, T. and Cowan, R. (1994). A stochastic tessellation of digital space. In Mathematical Morphology and Its Applications to Image Processing (J. Serra, ed.) 217–224. Dordrecht: Kluwer.
Lovejoy, S., Schertzer, D. and Watson, B. (1992). Radiative transfer and multifractal clouds: Theory and applications. International Radiation Symposium 92 108–111.
Møller, J. (2003). Shot noise Cox processes. Adv. in Appl. Probab. 35 614–640.
Pang, N. and Tzeng, W. (2004). Anomalous scaling of superrough growing surfaces: From correlation functions to residual local interfacial widths and scaling exponents. Phys. Rev. E 70 (036115).
Peirolo, R. and Scalerandi, M. (2004). Markovian model of growth and histologic progression in prostate cancer. Phys. Rev. E 70 (011902).
Prokešová, M., Hellmund, G. and Jensen, E.B.V. (2006). On spatio-temporal Lévy based Cox processes. In Proceedings of S4G, International Conference on Stereology, Spatial Statistics and Stochastic Geometry 111–116. Union of Czech Mathematics and Physicists.
Qi, A.S., Zheng, X., Du, C.Y. and An, B.S. (1993). A cellular automaton model of cancerous growth. J. Theor. Biol. 161 1–12.
Rajput, B. and Rosinski, J. (1989). Spectral representations of divisible processes. Probab. Theory Related Fields 82 451–487.
Richardson, D. (1973). Random growth in a tessellation. Proc. Cambridge Philos. Soc. 74 515–528.
Schmiegel, J. (2006). Self-scaling tumor growth. Phys. A 367 509–524.
Schmiegel, J., Barndorff-Nielsen, O. and Eggers, H. (2005). A class of spatio-temporal and causal stochastic processes, with application to multiscaling and multifractality. South African J. Science 101 513–519.
Schmiegel, J., Cleve, J., Eggers, H., Pearson, B. and Greiner, M. (2004). Stochastic energy-cascade model for 1 + 1 dimensional fully developed turbulence. Phys. Lett. A 320 247–253.
Sornette, D. and Ouillon, G. (2005). Multifractal scaling of thermally activated rupture processes. Phys. Rev. Lett. 94 (038501).
Steel, G.G. (1977). Growth Kinetics of Tumours. Oxford: Clarendon Press.
Stein, M. (2005). Space–time covariance functions. J. Amer. Statist. Assoc. 100 310–321.
Wolpert, R.L. and Ickstadt, K. (1998). Poisson/{G}amma random field models for spatial statistics. Biometrika 85 251–267.

2009 © Bernoulli Society for Mathematical Statistics and Probability