The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Generalized Linear Models with Functional Predictors
Open accessAbstract
Summary: We present a technique for extending generalized linear models to the situation where some of the predictor variables are observations from a curve or function. The technique is particularly useful when only fragments of each curve have been observed. We demonstrate, on both simulated and real data sets, how this approach can be used to perform linear, logistic and censored regression with functional predictors. In addition, we show how functional principal components can be used to gain insight into the relationship between the response and functional predictors. Finally, we extend the methodology to apply generalized linear models and principal components to standard missing data problems.
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Citations
Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
Random-Effects Models for Longitudinal Data
Laird, N. M., & Ware, J. H. (1982) · Biometrics · doi:10.2307/2529876 · Cited by 8,923
Some aspects of the spline smoothing approach to non-parametric regression curve fitting
Silverman, B. W. (1985) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1985.tb01327.x · Cited by 1,117
Cited by
Prediction in functional linear regression
Cai, T. T., & Hall, P. (2006) · The Annals of Statistics · doi:10.1214/009053606000000830 · Cited by 561
Morris, J. S. (2015) · Annual Review of Statistics and Its Application · doi:10.1146/annurev-statistics-010814-020413 · Cited by 423
The analysis of ordered categorical data: An overview and a survey of recent developments
Liu, I., & Agresti, A. (2005) · Test · doi:10.1007/bf02595397 · Cited by 301
Penalized Functional Regression
Goldsmith, J., Bobb, J., Crainiceanu, C. M., Caffo, B., & Reich, D. (2011) · Journal of Computational and Graphical Statistics · doi:10.1198/jcgs.2010.10007 · Cited by 288
Jacobson, N. C., Chow, S.-M., & Newman, M. G. (2019) · Behavior Research Methods · doi:10.3758/s13428-018-1101-0 · Cited by 63
Metadata
- Authors
- James, G. M.
- Venue
- Journal of the Royal Statistical Society Series B: Statistical Methodology
- Publisher
- Oxford University Press (OUP)
- Published
- 2002-08-01
- Volume
- 64
- Issue
- 3
- Pages
- 411-432
- ISSN
- 1369-7412 (ISSN-L)
- 1467-9868
- OpenAlex
- W2067165060
- Cited by
- 410
- References
- 4
- FWCI
- 5.76
- Type
- journal article
- Language
- en
- Open access
- bronze
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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