The generalized exponential (GE) distribution is an important lifetime distribution in survival analysis. It is considered as a suitable alternative to the most common lifetime distributions such as gamma and Weibull distributions. In this paper, a bivariate generalized exponential distribution (BVGE) is studied based on copula functions. Two illustrative examples are introduced to compare the studied estimation methods for the two proposed models using simulated and real data sets. Useful results are obtained.
Predictive analytics techniques are widely used in the application field, and the most common of these is fitting data with functions. The aim of function fittings is to predict the value of a response, by combing the regressors. Univariate probit and logit models are used for the same purposes when the response variable is binary. Both models used applied for the estimation of the functional relationship between response and regressors. The question of which model performs better comes to the mind. For this aim, a Monte Carlo simulation was performed to compare both the univariate probit and logit models under different conditions. In In this paper we considered the simulation of, employing latent variable approach with different sample sizes, cut points, and different correlations between response variable and regressors were taken into account. To make a comparison between univariate logit and probit models, Pearson residuals, deviations, Hosmer 186 Abeer H. Alsoruji et al. and Lemesshow, area under Receiver Operating Characteristic (ROC) curve, and Pseudo-R square statistics which are used for qualitative data analysis, were calculated and the results were interpreted.
Gaussian copula models are frequently used to extend univariate regression models to the multivariate case. The main benefit of the topic is that the specification of the regression model is conveniently separated from the dependence structure described in the familiar form of the correlation matrix of a multivariate Gaussian distribution [1]. This form of flexibility has been successfully employed in several complex applications including longitudinal data analysis, spatial statistics, genetics and time series. In this paper the Gaussian marginal copula regression applied to currency exchange rate data set by using log-return transformation and difference transformation according to AIC which are computed by applying the Gaussian marginal copula regression function in GCMR R package. 1054 Samia A. Adham et al.
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