Space-time regression modeling of tree growth using the skew-t distribution

Environmetrics

Volumn 21, Issue 7-8, 2010, Pages 817-833

  Nathoo, F S ^a  

^a UNIVERSITY OF VICTORIA   (Canada)

Author keywords

Juvenile tree growth; Markov random field; Process convolution; Skew t distribution; Spatially correlated random effects

Indexed keywords

BAYESIAN ANALYSIS; CONIFEROUS TREE; FOREST ECOSYSTEM; GAUSSIAN METHOD; GROWTH CURVE; MARKOV CHAIN; MULTIVARIATE ANALYSIS; PLANTATION; REGRESSION ANALYSIS; SKEWNESS;

PICEA GLAUCA;

EID: 78650654685     PISSN: 11804009     EISSN: 1099095X     Source Type: Journal    
DOI: 10.1002/env.1057     Document Type: Article

References (25)

1. Azzalini A, Capitanio A. 1999. Statistical applications of the multivariate skew normal distribution. Journal of the Royal Statistical Society, Series B 61: 579-602.
2. Azzalini A, Capitanio A. 2003. Distributions generated by perturbation of symmetry with emphasis on a multivariate skew-t distribution. Journal of the Royal Statistical Society, Series B 65: 367-389.
3. Banerjee S, Carlin BP, Gelfand AE. 2004. Hierarchical Modeling and Analysis for Spatial Data. Chapman & Hall/CRC: Boca Raton, FL.
4. Banerjee S, Johnson GA. 2006. Coregionalized single- and multi-resolution spatially-varying growth curve modelling with application to weed growth. Biometrics 61: 617-625.
5. Besag J, York JC, Mollié A. 1991. Bayesian image restoration, with two applications in spatial statistics (with discussion). Annals of the Institute of Statistical Mathematics 43: 1-59.
6. Calder CA. 2007. Dynamic factor process convolution models for multivariate space-time data with application to air quality assessment. Environmental and Ecological Statistics 14: 229-247.
7. Carlin BP, Banerjee S. 2003. Hierarchical multivariate CAR models for spatio-temporally correlated survival data (with discussion). In Bayesian Statistics 7, Bernardo JM, Bayarri MJ, Berger JO, Dawid AP, Heckerman D, Smith AFM, West M (eds). Oxford University Press: Oxford; 45-63.
8. Diggle PJ, Heagerty PJ, Liang KY, Zeger SL. 2002. Analysis of Longitudinal Data, 2nd edn. Oxford University Press: Oxford, UK.
9. Fernández C, Steel MFJ. 1998. On bayesian modeling of fat tails and skewness. Journal of the American Statistical Association 93: 359-371.
10. Gamerman D, Moreire A, Rue H. 2003. Space-varying regression models: specifications and simulation. Computational statistics and data analysis 42: 513-533.
11. Gelfand AE, Ghosh SK. 1998. Model choice: a minimum posterior predictive loss approach. Biometrika 85: 1-11.
12. Gelman A. 2006. Prior distributions for variance parameters in hierarchical models. Bayesian Analysis 1: 515-533.
13. Genton MG. 2004. Skew-elliptical Distributions and Their Applications: A Journey Beyond Normality. Chapman and Hall: Florida, USA.
14. Gustafson P. 1997. Large hierarchical bayesian analysis of multivariate survival data. Biometrics 53: 230-242.
15. Higdon D. 1998. A process-convolution approach to modeling temperatures in the North Atlantic Ocean. Journal of Ecological and Environmental Statistics 5: 173-190.
16. Higdon D. 2002. Space and space-time modeling using process convolutions. In Quantitative Methods for Current Environmental Issues, Anderson C, Barnett V, Chatwin PC, El-Shaarawi AH (eds). Springer-Verlag: London; 37-56.
17. Hossain MM, Lawson AB. 2010. Space-time Bayesian small area disease risk models: development and evaluation with a focus on cluster detection. Environmental and Ecological Statistics. 17: 73-95.
18. Jara A, Quintana FA, Martn ES. 2008. Linear effects mixed models with skew-elliptical distributions: a Bayesian approach. Computational Statistics and Data Analysis 52: 5033-5045.
19. Kern J. 2000. Bayesian process-convolution approaches to specifying spatial dependence structure, Ph.D. Thesis Duke University: Durham, NC; 27708.
20. Kim HM, Mallick BK. 2004. A Bayesian prediction using the skew Gaussian distribution. Journal of Statistical Planning and Inference 120: 85-8101.
21. Ma Y, Genton MG, Davidian M. 2004. Linear mixed effects models with flexible generalized skew-elliptical random effects. In Skew-elliptical Distributions Their Applications: Journey Beyond Normality, Genton MG (ed.). Chapman and Hall: Florida, USA; 339-358.
22. Nathoo FS. 2010. Joint spatial modeling of recurrent infection and growth with processes under intermittent observation. Biometrics. 66: 336-346.
23. Xia G, Gelfand AE. 2005. Stationary process approximation for the analysis of large spatial datasets. Technical Report, Institute of Statistics and Decision Science: Duke University.
24. Zimmerman D, Núñez-Antón V. 2001. Parametric modelling of growth curve data: an overview (with discussion). Test 10: 1-73.
25. Zhou W, Sansó B. 2007. Statistical inference for atmospheric transport models using process convolutions. Environmetrics 19: 87-101.