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Vector Generalized Additive Models

Yee, T. W., Wild, C. J.·1996·Journal of the Royal Statistical Society Series B: Statistical Methodology

Abstract

SUMMARY: Vector smoothing is used to extend the class of generalized additive models in a very natural way to include a class of multivariate regression models. The resulting models are called ‘vector generalized additive models‘. The class of models for which the methodology gives generalized additive extensions includes the multiple logistic regression model for nominal responses, the continuation ratio model and the proportional and non-proportional odds models for ordinal responses, and the bivariate probit and bivariate logistic models for correlated binary responses. They may also be applied to generalized estimating equations.

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Research works printed but not linked: 22

  1. Armstrong, 1989, Ordinal regression models for epidemiologic data, Am. J. Epidem., 129, 191, 10.1093/oxfordjournals.aje.a115109 · DOI · Paper page
  2. Ashford, 1970, Multi-variate probit analysis, Biometrics, 26, 535, 10.2307/2529107 · DOI · Paper page
  3. Buja, 1989, Linear smoothers and additive models (with discussion), Ann. Statist., 17, 453
  4. Fessler, 1991, Nonparametric fixed-interval smoothing with vector splines, IEEE Trans. Signal Process., 39, 852, 10.1109/78.80907 · DOI
  5. Green, 1994, Nonparametric Regression and Generalized Linear Models., 10.1007/978-1-4899-4473-3 · DOI · Paper page
  6. Hastie, 1987, Non-parametric logistic and proportional odds regression, Appl. Statist., 36, 260, 10.2307/2347785 · DOI
  7. Hastie, 1990, Generalized Additive Models.
  8. Hutchinson, 1985, Smoothing noisy data with spline functions, Numer. Math., 47, 99, 10.1007/BF01389878 · DOI · Paper page
  9. Liang, 1986, Longitudinal analysis using generalized linear models, Biometrika, 73, 13, 10.1093/biomet/73.1.13 · DOI · Paper page
  10. Liang, 1992, Multivariate regression analyses for categorical data (with discussion), J. R. Statist. Soc. B, 54, 3, 10.1111/j.2517-6161.1992.tb01862.x · DOI · Paper page
  11. McCullagh, 1980, Regression models for ordinal data (with discussion), J. R. Statist. Soc. B, 42, 109, 10.1111/j.2517-6161.1980.tb01109.x · DOI · Paper page
  12. Nelder, 1972, Generalized linear models, J. R. Statist. Soc. A, 135, 370, 10.2307/2344614 · DOI · Paper page
  13. Nerlove, 1973, Univariate and multivariate log-linear and logistic models
  14. Palmgren, 1989, Regression models for bivariate binary responses
  15. Peterson, 1990, Ordinal regression models for epidemiologic data, Am. J. Epidem., 131, 745, 10.1093/oxfordjournals.aje.a115562 · doi:10.1093/oxfordjournals.aje.a115562
  16. Peterson, 1992, Proportional odds model, Biometrics, 48, 325
  17. Prentice, 1988, Correlated binary regression with covariates specific to each binary observation, Biometrics, 44, 1033, 10.2307/2531733 · DOI · Paper page
  18. Wahba, 1983, Bayesian “confidence intervals” for the cross-validated smoothing spline, J. R. Statist. Soc. B, 45, 133, 10.1111/j.2517-6161.1983.tb01239.x · DOI · Paper page
  19. Wild, 1996, Additive extensions to generalized estimating equation methods, 58
  20. Yee, 1993, The analysis of binary data in quantitative plant ecology
  21. Yee, 1994, Vector splines and the vector additive model
  22. Yee, 1994, Vector generalized additive models

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Metadata

Authors
Yee, T. W., & Wild, C. J.
Venue
Journal of the Royal Statistical Society Series B: Statistical Methodology
Published
1996-09-01
Volume
58
Issue
3
Pages
481-493
ISSN
  • 1369-7412 (ISSN-L)
  • 1467-9868
OpenAlex
W53657057
Cited by
423
References
2
FWCI
1.98
Type
journal article
Language
en
Open access
closed

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Yee, T. W., & Wild, C. J. (1996). Vector Generalized Additive Models. Journal of the Royal Statistical Society Series B: Statistical Methodology. https://doi.org/10.1111/j.2517-6161.1996.tb02095.x

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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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