Joint spatial modeling of recurrent infection and growth with processes under intermittent observation

Biometrics

Volumn 66, Issue 2, 2010, Pages 336-346

  Nathoo, F S ^a  

^a UNIVERSITY OF VICTORIA   (Canada)

Author keywords

Hybrid Monte Carlo; Joint modeling of survival and longitudinal data; Multivariate process convolution; Pine weevil; Recurrent events; Spatially varying trajectory

Indexed keywords

ANIMALS; HAZARDS; MONTE CARLO METHODS; REFORESTATION; SAMPLING; TRAJECTORIES;

HYBRID MONTE CARLO; JOINT MODELING OF SURVIVAL AND LONGITUDINAL DATA; JOINT MODELS; LONGITUDINAL DATA; MULTIVARIATE PROCESS; MULTIVARIATE PROCESS CONVOLUTION; PINE WEEVIL; RECURRENT EVENTS; SPATIALLY VARYING TRAJECTORY; SURVIVAL DATA;

CONVOLUTION;

BAYESIAN ANALYSIS; COVARIANCE ANALYSIS; EVERGREEN TREE; GROWTH MODELING; HAZARD ASSESSMENT; MONTE CARLO ANALYSIS; PANEL DATA; PARAMETERIZATION; PARASITE INFESTATION; RECURRENCE INTERVAL; REFORESTATION; SAMPLING; SPATIAL VARIATION; SPATIOTEMPORAL ANALYSIS; SURVIVAL; TREE;

BRITISH COLUMBIA; CANADA;

PICEA GLAUCA;

ANIMAL; GROWTH, DEVELOPMENT AND AGING; HUMAN; INFECTION; LONGITUDINAL STUDY; PARASITOLOGY; PATHOGENICITY; PLANT DISEASE; RECURRENT DISEASE; SPRUCE; STATISTICAL MODEL; WEEVIL;

ANIMALS; HUMANS; INFECTION; LONGITUDINAL STUDIES; MODELS, STATISTICAL; PICEA; PLANT DISEASES; RECURRENCE; WEEVILS;

EID: 77952975344     PISSN: 0006341X     EISSN: 15410420     Source Type: Journal    
DOI: 10.1111/j.1541-0420.2009.01305.x     Document Type: Article

References (20)

1. Baladandayuthapani, V., Mallick, B. K., Hong, M. Y., Lupton, J. R., Turner, N. D., and Carroll, R. J. (2008). Bayesian hierarchical spatially correlated functional data analysis with application to colon carcinogenesis. Biometrics 64, 64 73.
2. Banerjee, S. and Johnson, G. A. (2006). Coregionalized single- and multi-resolution spatially-varying growth curve modelling with application to weed growth. Biometrics 61, 617 625.
3. Fox, J. C., Bi, H., and Ades, P. K. (2007). Spatial dependence and individual tree growth models I. Characterising spatial dependence. Forest Ecology and Management 245, 10 19.
4. Garcia, O. (1983). A stochastic differential equation model for the height growth of forest stands. Biometrics 39, 1059 1072.
5. Gelfand, A. E. and Dey, D. K. (1994). Bayesian model choice: Asymptotics and exact calculations. Journal of the Royal Statistical Society, Series B 56, 501 514.
6. Gelman, A., Meng, X.-L., and Stern, H. (1996). Posterior predicitve model assessment of model fitness via realized discrepencies. Statistica Sinica 6, 733 807. (Pubitemid 126706389)
7. Gustafson, P. (1997). Large hierarchical Bayesian analysis of multivariate survival data. Biometrics 53, 230 242.
8. Higdon, D. M. (1998). A process-convolution approach to modeling temperatures in the North Atlantic ocean. Journal of Ecological and Environmental Statistics 5, 173 190.
9. Higdon, D. M. (2002). Space and Space-time Modeling Using Process Convolutions, 37 54.
10. Ishwaran, H. (1999). Applications of hybrid Monte Carlo to Bayesian generalized linear models: Quasicomplete separation and neural networks. Journal of Computational and Graphical Statistics 8, 779 799.
11. King, J. N., Yanchuck, A. D., Kiss, G. K., and Alfaro, R. I. (1997). Genetic and phenotypic relationships between weevil resistance and height growth in spruce populations of British Columbia. Canadian Journal of Forestry Research 27, 732 739.
12. Nathoo, F. S. and Dean, C. B. (2007). A mixed mover-stayer model for spatio-temporal two-state processes. Biometrics 63, 881 891.
13. Roberts, G. O. and Rosenthal, J. S. (2001). Optimal scaling for various Metropolis-Hastings algorithms. Statistical Science 16, 351 367.
14. Royle, J. A. and Nychka, D. (1998). An algorithm for the construction of spatial coverage designs with implementation in SPLUS. Computers and Geosciences 24, 479 488.
15. Stroud, J. R., Müller, P., and Sansó, B. (2001). Dynamic models for spatiotemporal data. Journal of the Royal Statistical Society, Series B 63, 673 689. (Pubitemid 33402066)
16. Tsiatis, A. and Davidian, M. (2004). An overview of joint modeling of longitudinal and time-to-event data. Statistica Sinica 14, 793 818.
17. Ver Hoef, J. M. and Barry, R. P. (1998). Constructing and fitting models for cokriging and multivariable spatial prediction. Journal of Statistical Planning and Inference 69, 275 294.
18. Xia, G. and Gelfand, A. (2005). Stationary process approximation for the analysis of large spatial datasets. Technical report
19. Zhu, J., Huang, H. C., and Wu, J. (2005). Modeling spatial-temporal binary data using Markov random fields. Journal of Agricultural, Biological, and Environmental Statistics 10, 212 225.
20. 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, 84 98.