364 Handbook of Discrete-Valued Time Series
Currin, C., Mitchell, T., Morris, M., and Ylvisaker, D. (1991). Bayesian prediction of deterministic
functions, with applications to the design and analysis of computer experiments. Journal of the
American Statistical Association, 86(416):953–963.
Dancik, G. M., Jones, D. E., and Dorman, K. S. (2010). Parameter estimation and sensitivity
analysis in an agent-based model of Leishmania major infection. Journal of Theoretical Biology,
262(3):398–412.
Filatova, T., Verburg, P. H., Parker, D. C., and Stannard, C. A. (2013). Spatial agent-based models for
socio-ecological systems: Challenges and prospects. Environmental Modelling & Software, 45:1–7.
Frolov, S., Baptista, A. M., Leen, T. K., Lu, Z., and van der Merwe, R. (2009). Fast data assimilation
using a nonlinear Kalman lter and a model surrogate: An application to the columbia river
estuary. Dynamics of Atmospheres and Oceans, 48(1):16–45.
Gilbert, N. (2008). Agent-Based Models. Sage Publications, Los Angeles, CA.
Grimm, V. and Railsback, S. F. (2005). Individual-Based Modeling and Ecology. Princeton University
Press, Princeton, NJ.
Guyon, X. (1995). Random Fields on a Network: Modeling, Statistics, and Applications. Springer-Verlag,
New York.
Higdon, D., Gattiker, J., Williams, B., and Rightley, M. (2008). Computer model calibration using
high-dimensional output. JournaloftheAmericanStatisticalAssociation, 103(482), 570–583.
Higdon, D., Kennedy, M., Cavendish, J. C., Cafeo, J. A., and Ryne, R. D. (2004). Combining eld data
and computer simulations for calibration and prediction. SIAM Journal on Scientic Computing,
26(2):448–466.
Holan, S. H. and Wikle, C. K. (2015). Hierarchical dynamic generalized linear mixed models for
discrete-valued spatio-temporal data. In R. A. Davis, S. H. Holan, R. Lund and N. Ravishanker,
eds., pp. 327–348. Handbook of Discrete-Valued Time Series, Chapman & Hall, Boca Raton, FL.
Hooten, M. B., Johnson, D. S., Hanks, E. M., and Lowry, J. H. (2010). Agent-based inference for
animal movement and selection. Journal of Agricultural, Biological, and Environmental Statistics,
15(4):523–538.
Hooten, M. B., Leeds, W. B., Fiechter, J., and Wikle, C. K. (2011). Assessing rst-order emulator infer-
ence for physical parameters in nonlinear mechanistic models. Journal of Agricultural, Biological,
and Environmental Statistics, 16(4):475–494.
Hooten, M. B. and Wikle, C. K. (2010). Statistical agent-based models for discrete spatio-temporal
systems. Journal of the American Statistical Association, 105(489):236–248.
Keeling, M. J. and Rohani, P. (2008). Modeling Infectious Diseases in Humans and Animals.Princeton
University Press, Princeton, NJ.
Kennedy, M. C. and O’Hagan, A. (2001). Bayesian calibration of computer models. Journal of the Royal
Statistical Society: Series B (Statistical Methodology), 63(3):425–464.
Lagarrigues, G., Jabot, F., Lafond, V., and Courbaud, B. (2014). Approximate Bayesian computation to
recalibrate individual-based models with population data: Illustration with a forest simulation
model. Ecological Modelling. doi:10.1016/j.ecolmodel.2014.09.023.
Leeds, W., Wikle, C., and Fiechter, J. (2014). Emulator-assisted reduced-rank ecological data assimi-
lation for nonlinear multivariate dynamical spatio-temporal processes. Statistical Methodology,
17:126–138.
Leeds, W., Wikle, C., Fiechter, J., Brown, J., and Milliff, R. (2013). Modeling 3-d spatio-temporal
biogeochemical processes with a forest of 1-d statistical emulators. Environmetrics, 24(1):1–12.
Marin, J.-M., Pudlo, P., Robert, C. P., and Ryder, R. J. (2012). Approximate Bayesian computational
methods. Statistics and Computing, 22(6):1167–1180.
OHagan, A. (2006). Bayesian analysis of computer code outputs: A tutorial. Reliability Engineering &
System Safety, 91(10):1290–1300.
Olfati-Saber, R. (2006). Flocking for multi-agent dynamic systems: Algorithms and theory. IEEE
Transactions on Automatic Control, 51(3):401–420.
Parry, H. R., Topping, C. J., Kennedy, M. C., Boatman, N. D., and Murray, A. W. (2013). A Bayesian
sensitivity analysis applied to an agent-based model of bird population response to landscape
change. Environmental Modelling & Software, 45:104–115.
Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data 365
Piou, C., Berger, U., and Grimm, V. (2009). Proposing an information criterion for individual-
based models developed in a pattern-oriented modelling framework. Ecological Modelling,
220(17):1957–1967.
Rasmussen, J. G., Møller, J., Aukema, B. H., Raffa, K. F., and Zhu, J. (2007). Continuous time mod-
elling of dynamical spatial lattice data observed at sparsely distributed times. Journal of the Royal
Statistical Society: Series B (Statistical Methodology), 69(4):701–713.
Sacks, J., Welch, W. J., Mitchell, T. J., and Wynn, H. P. (1989). Design and analysis of computer
experiments. Statistical Science, 4(4):409–423.
Sattenspiel, L. (2009). The Geographic Spread of Infectious Diseases: Models and Applications.Princeton
University Press, Princeton, NJ.
Smith, D. L., Lucey, B., Waller, L. A., Childs, J. E., and Real, L. A. (2002). Predicting the spatial
dynamics of rabies epidemics on heterogeneous landscapes. Proceedings of the National Academy
of Sciences, 99(6):3668–3672.
Tavaré, S., Balding, D. J., Grifths, R. C., and Donnelly, P. (1997). Inferring coalescence times from
DNA sequence data. Genetics, 145(2):505–518.
Toni, T., Welch, D., Strelkowa, N., Ipsen, A., and Stumpf, M. P. (2009). Approximate Bayesian com-
putation scheme for parameter inference and model selection in dynamical systems. Journal of
the Royal Society Interface, 6(31):187–202.
van der Merwe, R., Leen, T. K., Lu, Z., Frolov, S., and Baptista, A. M. (2007). Fast neural network
surrogates for very high dimensional physics-based models in computational oceanography.
Neural Networks, 20(4):462–478.
Wheeler, D. C. and Waller, L. A. (2008). Mountains, valleys, and rivers: The transmission of raccoon
rabies over a heterogeneous landscape. Journal of Agricultural, Biological, and Environmental
Statistics, 13(4):388–406.
Wikle, C. K. (2002). A kernel-based spectral model for non-Gaussian spatio-temporal processes.
Statistical Modelling, 2(4):299–314.
Wikle, C. K. (2003). Hierarchical Bayesian models for predicting the spread of ecological processes.
Ecology, 84(6):1382–1394.
Wikle, C. K. and Holan, S. H. (2011). Polynomial nonlinear spatio-temporal integro-difference
equation models. Journal of Time Series Analysis, 32(4):339–350.
Wikle, C. K. and Hooten, M. B. (2010). A general science-based framework for dynamical spatio-
temporal models. Test, 19(3):417–451.
Wolfram, S. (1984). Cellular automata as models of complexity. Nature, 311:419–423.
Zheng, Y. and Zhu, J. (2008). Markov chain Monte Carlo for a spatial-temporal autologistic regression
model. Journal of Computational and Graphical Statistics, 17(1):123–137.
Zhu, J., Huang, H.-C., and Wu, J. (2005). Modeling spatial-temporal binary data using Markov
random elds. Journal of Agricultural, Biological, and Environmental Statistics, 10(2):212–225.
Zhu, J. and Zheng, Y. (2015). Autologistic regression models for spatio-temporal binary data.
In R. A. Davis, S. H. Holan, R. Lund and N. Ravishanker, eds., Handbook of Discrete-Valued Time
Series, pp. 367–386. Chapman & Hall, Boca Raton, FL.
Zhu, J., Zheng, Y., Carroll, A. L., and Aukema, B. H. (2008). Autologistic regression analysis of spatial-
temporal binary data via Monte Carlo maximum likelihood. Journal of Agricultural, Biological,
and Environmental Statistics, 13(1):84–98.
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