(2012). Gaussian copula marginal regression. Electron. J. Stat. 6 1517-1549. MR2988457Guido Masarotto, Cristiano Varin, et al. Gaussian copula marginal regression. Electronic Journal of Statistics, 6:1517-1549, 2012.Masarotto G, Varin C (2012). "Gaussian Copula Marginal Regression." ...
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 pro... G Masarotto,C Varin - 《Electronic Journal of Statistics》 被引量: 32发表: 2017年 A Gaussian mi...
and Varin, C., 2012, Gaussian copula marginal regression. Electronic Journal of Statistics, 6, 1517–1549. [16] McCulloch, C., 2007, Joint modeling of mixed outcome types using latent variables. Statis- tical Methods in Medical Research, 17, 1–21. [17] Nikoloulopoulos, A. K. and ...
Liu, "A Gaussian Copula Regression Model for Movie Box-office Revenue Prediction with Social Media," Communications in Computer and Information Science Social Media Processing, pp. 28-37, 2015.Junwen DUAN, Xiao DING, Ting LIU.A : Gaussian copula regression model for movie box-office revenues ...
To address these problems, we investigate a novel Gaussian copula regression model. Based on this model, we do not need to make any prior assumptions about the marginal distributions of the features. In particular, we perform a cumulative probability estimation on each of the smoothed features. ...
An extension to include flexible Gaussian copula models that relaxes the Gaussian marginal assumption is also proposed. We illustrate the effectiveness of the proposed model on a variety of synthetic and benchmark data sets for regression and classification. We also consider the problem of combining ...
To address this problem, we propose a novel class of Bayesian Gaussian copula factor models that decouple the latent factors from the marginal distributions. A semiparametric specification for the marginals based on the extended rank likelihood yields straightforward implementation and substantial ...
Frank copulaLogistic regressionStatistical classifierStatistical pattern recognition has attracted great interest due to its applicability and to the advances in technology and computing. Significant research has been done in areas such as automatic character recognition, medical diagnostics, and data mining....
Yu, Khan, and Garaniya (2015a)proposed a probabilistic multivariate method for fault diagnosis of industrial processes. The study employed a Gaussian copula based on rank correlation to model dependencies and nonlinearity of process variables. The technique is useful in handling nonlinearities; however...
The use of such copula techniques broadens the scope of these diagnostics. For more information see the supplementary material (see Appendix A). Although for this study the within-language covariances were not significantly different this may not always be the case. In such instances, pooling the...