gcmr

R package gcmr: Gaussian copula marginal regression. 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.


References in zbMATH (referenced in 31 articles , 2 standard articles )

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  1. Papadopoulos, Alecos: Accounting for endogeneity in regression models using copulas: a step-by-step guide for empirical studies (2022)
  2. Sheikhi, Ayyub; Arad, Fereshteh; Mesiar, Radko: A heteroscedasticity diagnostic of a regression analysis with copula dependent random variables (2022)
  3. Alqawba, Mohammed; Diawara, Norou: Copula-based Markov zero-inflated count time series models with application (2021)
  4. Ribeiro, Vinícius S. O.; Nobre, Juvêncio S.; dos Santos, José Roberto S.; Azevedo, Caio L. N.: Beta rectangular regression models to longitudinal data (2021)
  5. Baghfalaki, Taban; Ganjali, Mojtaba: A transition model for analyzing multivariate longitudinal data using Gaussian copula approach (2020)
  6. Guo, Feng; Ma, Wei; Wang, Lei: Semiparametric estimation of copula models with nonignorable missing data (2020)
  7. Zhao, Yue; Gijbels, Irène; van Keilegom, Ingrid: Inference for semiparametric Gaussian copula model adjusted for linear regression using residual ranks (2020)
  8. Alqawba, Mohammed; Diawara, Norou; Rao Chaganty, N.: Zero-inflated count time series models using Gaussian copula (2019)
  9. Côté, Marie-Pier; Genest, Christian; Omelka, Marek: Rank-based inference tools for copula regression, with property and casualty insurance applications (2019)
  10. Lennon, Hannah; Yuan, Jingsong: Estimation of a digitised Gaussian ARMA model by Monte Carlo expectation maximisation (2019)
  11. Meulman, Jacqueline J.; van der Kooij, Anita J.; Duisters, Kevin L. W.: ROS regression: integrating regularization with optimal scaling regression (2019)
  12. Petterle, Ricardo Rasmussen; Bonat, Wagner Hugo; Scarpin, Cassius Tadeu: Quasi-beta longitudinal regression model applied to water quality index data (2019)
  13. He, Yong; Zhang, Xinsheng; Zhang, Liwen: Variable selection for high dimensional Gaussian copula regression model: an adaptive hypothesis testing procedure (2018)
  14. Popovic, Gordana C.; Hui, Francis K. C.; Warton, David I.: A general algorithm for covariance modeling of discrete data (2018)
  15. Bonat, W. H.; Olivero, J.; Grande-Vega, M.; Farfán, M. A.; Fa, J. E.: Modelling the covariance structure in marginal multivariate count models: hunting in Bioko Island (2017)
  16. Dey, Rajib; Islam, M. Ataharul: A conditional count model for repeated count data and its application to GEE approach (2017)
  17. Guido Masarotto and Cristiano Varin: Gaussian Copula Regression in R (2017) not zbMATH
  18. Huang, A.: On generalised estimating equations for vector regression (2017)
  19. Tobias Liboschik; Konstantinos Fokianos; Roland Fried: tscount: An R Package for Analysis of Count Time Series Following Generalized Linear Models (2017) not zbMATH
  20. Ding, Wei; Song, Peter X.-K.: EM algorithm in Gaussian copula with missing data (2016)

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