The DOI matches its Crossref record; 21 of the 21 references it cites were found in a registry or an index.
Clustering for Sparsely Sampled Functional Data
Abstract
We develop a flexible model-based procedure for clustering functional data. The technique can be applied to all types of curve data but is particularly useful when individuals are observed at a sparse set of time points. In addition to producing final cluster assignments, the procedure generates predictions and confidence intervals for missing portions of curves. Our approach also provides many useful tools for evaluating the resulting models. Clustering can be assessed visually via low-dimensional representations of the curves, and the regions of greatest separation between clusters can be determined using a discriminant function. Finally, we extend the model to handle multiple functional and finite-dimensional covariates and show how it can be applied to standard finite-dimensional clustering problems involving missing data.
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Cites
Estimating the Dimension of a Model
Schwarz, G. (1978) · The Annals of Statistics · doi:10.1214/aos/1176344136 · Cited by 47,033
Algorithm AS 136: A K-Means Clustering Algorithm
Hartigan, J. A., & Wong, M. A. (1979) · Applied Statistics · doi:10.2307/2346830 · Cited by 14,523
de Boor, C. (1978) · Applied Mathematical Sciences · doi:10.1007/978-1-4612-6333-3 · Cited by 11,944
Finding groups in data: an introduction to cluster analysis
(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,474
Regularized Discriminant Analysis
Friedman, J. H. (1989) · Journal of the American Statistical Association · doi:10.1080/01621459.1989.10478752 · Cited by 2,421
Model-Based Gaussian and Non-Gaussian Clustering
Banfield, J. D., & Raftery, A. E. (1993) · Biometrics · doi:10.2307/2532201 · Cited by 2,395
Computing Bayes Factors by Combining Simulation and Asymptotic Approximations
Diciccio, T. J., Kass, R. E., Raftery, A., & Wasserman, L. (1997) · Journal of the American Statistical Association · doi:10.1080/01621459.1997.10474045 · Cited by 1,943
Nonparametric Regression and Generalized Linear Models: A Roughness Penalty Approach.
Best, D. J., Green, P. J., & Silverman, B. W. (1994) · Biometrics · doi:10.2307/2533467 · Cited by 1,177
Gaussian parsimonious clustering models
Celeux, G., & Govaert, G. (1995) · Pattern Recognition · doi:10.1016/0031-3203(94)00125-6 · Cited by 872
Penalized Discriminant Analysis
Hastie, T., Buja, A., & Tibshirani, R. (1995) · The Annals of Statistics · doi:10.1214/aos/1176324456 · Cited by 849
Cited by
Functional Data Analysis for Sparse Longitudinal Data
Yao, F., Müller, H.-G., & Wang, J.-L. (2005) · Journal of the American Statistical Association · doi:10.1198/016214504000001745 · Cited by 1,595
Significance analysis of time course microarray experiments
Storey, J. D., Xiao, W., Leek, J. T., Tompkins, R. G., & Davis, R. W. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504609102 · Cited by 567
KmL: k-means for longitudinal data
Genolini, C., & Falissard, B. (2010) · Computational Statistics · doi:10.1007/s00180-009-0178-4 · Cited by 275
Modeling and predicting osteoarthritis progression: data from the osteoarthritis initiative
Halilaj, E., Le, Y., Hicks, J. L., Hastie, T. J., & Delp, S. L. (2018) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2018.08.003 · Cited by 95
Dharmaratne, A. D. V. T. T., Dini, S., O’Flaherty, K., Price, D. J., Beeson, J., McGready, R., Nosten, F., Fowkes, F. J. I., Simpson, J. A., & Zaloumis, S. G. (2022) · Malaria Journal · doi:10.1186/s12936-022-04111-y · Cited by 20
Metadata
- Authors
- James, G. M., & Sugar, C. A.
- Venue
- Journal of the American Statistical Association
- Publisher
- Informa UK Limited
- Published
- 2003-06
- Volume
- 98
- Issue
- 462
- Pages
- 397-408
- ISSN
- 0162-1459 (ISSN-L)
- 1537-274X
- OpenAlex
- W1988997815
- Cited by
- 502
- References
- 21
- FWCI
- 12.37
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
