Rejoinder: The 2005 Neyman Lecture: Dynamic Indeterminism in Science
David R. Brillinger
Source: Statist. Sci. Volume 23, Number 1 (2008), 76-77.
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Links and Identifiers
Permanent link to this document: http://projecteuclid.org/euclid.ss/1215441285
Digital Object Identifier: doi:10.1214/08-STS246REJ
References
Brillinger, D. R. (1983). Statistical inference for random processes. In Proceedings of the International Congress of Mathematicians 1, 2 1049–1061. PWN, Warsaw.
Mathematical Reviews (MathSciNet):
MR804757
Brillinger, D. R. (2000). Some examples of random process environmental data analysis. In Handbook of Statistics 18 (P. K. Sen and C. R. Rao, eds.) 33–56. North-Holland, Amsterdam.
Brillinger, D. R., Guckenheimer, J., Guttorp, P. and Oster, G. (1980). Empirical modeling of population time series data: The case of age and density dependent vital rates. Lectures Mathematics Life Sciences 13 65–90.
Brillinger, D. R., Preisler, H. K., Ager, A. A. Kie, J. and Stewart, B. S. (2001). Modelling movements of free-ranging animals. Technical Report 610. UCB Statistics.
Guttorp, P. M. (1980). Statistical modelling of population processes. Ph.D. thesis, Univ. California, Berkeley.
Jonsen, I., Mills Flemming, J. and Myers, R. (2005). Robust state-space modelling of animal movement data. Ecology 86 2874–2880.
Lehmann, E. (2008). Reminiscences of a Statistician: The Company I Kept. Springer, New York.
Mathematical Reviews (MathSciNet):
MR2367933
Neyman, J. and Scott, E. L. (1974). Rain stimulation experiments: Design and evaluation. In Proc. WMO/IAMAP Sci. Conf. Weather Modification 449–457. WMO, Geneva.
Yang, G. L. (1968). Contagion in stochastic models for epidemics. Ann. Math. Statist. 39 1863–1889.
Mathematical Reviews (MathSciNet):
MR237022
Digital Object Identifier: doi:10.1214/aoms/1177698018
Project Euclid: euclid.aoms/1177698018
