Stringing high-dimensional data for functional analysis

Journal of the American Statistical Association

Volumn 106, Issue 493, 2011, Pages 275-284

  Chen, Kun ^a   Chen, Kehui ^c   Müller, Hans Georg ^c   Wang, Jane Ling ^b  

^a DANA FARBER CANCER INSTITUTE   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c NONE

Author keywords

Functional cox model; Functional data analysis; Gene expression; High dimensional predictors; Multidimensional scaling; Prediction; Tree rings

Indexed keywords

EID: 79954480790     PISSN: 01621459     EISSN: None     Source Type: Journal    
DOI: 10.1198/jasa.2011.tm10314     Document Type: Article

References (30)

1. Andrews, D. (1972), "Plots of High-Dimensional Data," Biometrics, 28, 125-136. [276]
2. Ash, R. B., and Gardner, M. F. (1975), Topics in Stochastic Processes, New York: Academic Press. [277]
3. Bair, E., and Tibshirani, R. (2004), "Semi-Supervised Methods to Predict Patient Survival From Gene Expression Data," PLoS Biology, 2, 511-522. [276,282]
4. Bair, E., Hastie, T., Paul, D., and Tibshirani, R. (2006), "Prediction by Super- vised Principal Components," Journal of the American Statistical Associa- tion, 101, 119-137. [276,282]
5. Bengio, Y., Monperrus, M., and Larochelle, H. (2006), "Nonlocal Estimation of Manifold Structure," Neural Computation, 18, 2509-2528. [283]
6. Borg, I., and Groenen, P. (2005), Modern Multidimensional Scaling, New York: Springer. [276]
7. Bøvelstad, H., Nygård, S., Størvold, H., Aldrin, M., Borgan, Ø., Frigessi, A., and Lingjærde, O. (2007), "Predicting Survival From Microarray Data-A Comparative Study," Bioinformatics, 23, 2080-2087. [282,283]
8. Bunn, A. G., Graumlich, L. J., and Urban, D. L. (2005), "Trends in Twentieth- Century Tree Growth at High Elevations in the Sierra Nevada and White Mountains, USA," The Holocene, 15, 481. [280]
9. Cook, E., and Kairiukstis, L. (eds.) (1990), Methods of Dendrochronology, Dordrecht: Kluwer Academic. [280]
10. Cox, T. F., and Cox, M. A. A. (2001), Multidimensional Scaling, London: Chap- man & Hall. [275,276]
11. Embrechts, P., and Herzberg, A. (1991), "Variations of Andrews' Plots," International Statistical Review, 59, 175-194. [276]
12. Garcia-Osorio, C., and Fyfe, C. (2005), "Visualization of High-Dimensional Data via Orthogonal Curves," Journal of Universal Computer Science, 11, 1806-1819. [276]
13. Hall, P., Müller, H.-G., and Yao, F. (2008), "Modeling Sparse Generalized Longitudinal Observations With Latent Gaussian Processes," Journal of the Royal Statistical Society, Ser. B, 70, 730-723. [278]
14. Hubert, L., and Arabie, P. (1986), "Unidimensional Scaling and Combinatorial Optimization," in Multidimensional Data Analysis, eds. J. De Leeuw, W. Heiser, and J. Meulman, Leiden: DSWO Press, pp. 181-196. [276]
15. Hubert, L., Arabie, P., and Meulman, J. (1997), "Linear and Circular Unidimensional Scaling for Symmetric Proximity Matrices," British Journal of Mathematical and Statistical Psychology, 50, 253-284. 276]
16. Kruskal, J. (1964), "Nonmetric Multidimensional Scaling: A Numerical Method," Psychometrika, 29, 115-129. [276]
17. LaMarche Jr., V. C. (1978), "Tree-Ring Evidence of Past Climatic Variability," Nature, 276, 334-338. [280]
18. Li, L. X., and Li, H. Z. (2004), "Dimension Reduction Methods for Microarrays With Application to Censored Survival Data," Bioinformatics, 20, 3406-3412. [276]
19. Müller, H.-G. (2005), "Functional Modelling and Classification of Longitudinal Data," Scandinavian Journal of Statistics, 32, 223-240. [278,281]
20. Nguyen, D., and Rocke, D. M. (2002), "Partial Least Squares Proportional Haz- ard Regression for Application to DNA Microarray Data," Bioinformatics, 18,120-127. [276]
21. Oberhuber, W., and Kofler, W. (2000), "Topographic Influences on Radial Growth of Scots Pine (Pinus sylvestris L.) at Small Spatial Scales," Plant Ecology, 146, 231-240. [280]
22. Pliner, V. (1996), "Metric Unidimensional Scaling and Global Optimization," Journal of Classification, 13, 3-18. [276]
23. Ramsay, J. (1982), "Some Statistical Approaches to Multidimensional Scaling Data," Journal of the Royal Statistical Society, Ser. A, 145, 285-312. [276]
24. Ramsay, J. O., and Silverman, B. W. (2005), Functional Data Analysis, New York: Springer. [275]
25. Rice, J. A., and Silverman, B. W. (1991), "Estimating the Mean and Covariance Structure Nonparametrically When the Data Are Curves," Journal of the Royal Statistical Society, Ser. B, 53, 233-243. [275,280]
26. Rosenwald, A., Wright, G., Chan, W. C., Connors, J. M., Campo, E., Fisher, R., Gascoyne, R., Muller-Hermelink, H., Smeland, E., and Staudt, L. (2002), "The Use of Molecular Profiling to Predict Survival After Themotheropy for Diffuse Large-B-Cell Lymphoma," New England Journal of Medicine, 346,1937-1947. [276,281,282]
27. Tenenbaum, J. B., De Silva, V., and Langford, J. C. (2000), "A Global Geometric Framework for Nonlinear Dimensionality Reduction," Science, 290 (5500),2319-2323. [283]
28. Witten, D., and Tibshirani, R. (2010), "Survival Analysis With High-Dimensional Covariates," Statistical Methods in Medical Research, 19, 29-51. [282]
29. Wu, P., and Müller, H.-G. (2010), "Functional Embedding for the Classification of Gene Expression Profiles," Bioinformatics, 26, 509-517. [276]
30. Yao, F., Müller, H.-G., and Wang, J.-L. (2005), "Functional Data Analysis for Sparse Longitudinal Data," Journal of the American Statistical Association, 100,577-590. [275,280-282]