The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Gaussian copula marginal regression
Open accessAbstract
This paper identifies and develops the class of Gaussian copula models for marginal regression analysis of non-normal dependent observations. The class provides a natural extension of traditional linear regression models with normal correlated errors. Any kind of continuous, discrete and categorical responses is allowed. Dependence is conveniently modelled in terms of multivariate normal errors. Inference is performed through a likelihood approach. While the likelihood function is available in closed-form for continuous responses, in the non-continuous setting numerical approximations are used. Residual analysis and a specification test are suggested for validating the adequacy of the assumed multivariate model. Methodology is implemented in a R package called gcmr. Illustrations include simulations and real data applications regarding time series, cross-design data, longitudinal studies, survival analysis and spatial regression.
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Citations
Cites
McCullagh, P., & Nelder, J. A. (1989) · doi:10.1007/978-1-4899-3242-6 · Cited by 16,836
Discrete Choice Methods with Simulation
Train, K. E. (2001) · doi:10.1017/cbo9780511805271 · Cited by 7,784
Cressie, N. A. C. (1993) · Wiley Series in Probability and Statistics · doi:10.1002/9781119115151 · Cited by 6,507
Joint regression analysis of correlated data using Gaussian copulas
Song, P. X. ‐., Li, M., & Yuan, Y. (2009) · Biometrics · doi:10.1111/j.1541-0420.2008.01058.x · Cited by 201
The Evaluation of General Non-Centred Orthant Probabilities
Miwa, T., Hayter, A. J., & Kuriki, S. (2003) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/1467-9868.00382 · Cited by 100
Metadata
- Authors
- Masarotto, G., & Varin, C.
- Venue
- Electronic Journal of Statistics
- Publisher
- Institute of Mathematical Statistics
- Published
- 2012-01-01
- Volume
- 6
- Issue
- none
- Pages
- 1517-1549
- ISSN
- 1935-7524 (ISSN-L)
- OpenAlex
- W2015615053
- Cited by
- 182
- References
- 5
- FWCI
- 12.74
- Type
- journal article
- Language
- en
- Open access
- gold
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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.
