A pseudo-slug flow is a type of intermittent flow characterized by short, frothy, chaotic slugs that have a structure velocity lower than the mixture velocity and are not fully formed. It is essential to accurately estimate the transition from conventional slug (SL) flow to pseudo-slug (PSL) flow, and from SL to churn (CH), by precisely predicting the pressure losses. Recent research has showed that PSL and CH flows comprise a significant portion of the conventional flow pattern maps. This is particularly true in wellbores and pipelines with highly deviated large-diameter gas-condensate wellbores and pipelines. Several theoretical and experimental works studied the behavior of PSL and CH flows; however, few models have been suggested to pre

Semi-parametric models analysis is one of the most interesting subjects in recent studies due to give an efficient model estimation. The problem when the response variable has one of two values either 0 ( no response) or one – with response which is called the logistic regression model.

We compare two methods Bayesian and . Then the results were compared using MSe criteria.

A simulation had been used to study the empirical behavior for the Logistic model , with  different sample sizes and variances. The results using represent that the Bayesian method is better than the   at small samples sizes.

Generalized Additive Model has been considered as a multivariate smoother that appeared recently in Nonparametric Regression Analysis. Thus, this research is devoted to study the mixed situation, i.e. for the phenomena that changes its behaviour from linear (with known functional form) represented in parametric part, to nonlinear (with unknown functional form: here, smoothing spline) represented in nonparametric part of the model. Furthermore, we propose robust semiparametric GAM estimator, which compared with two other existed techniques.

The Dagum Regression Model, introduced to address limitations in traditional econometric models, provides enhanced flexibility for analyzing data characterized by heavy tails and asymmetry, which is common in income and wealth distributions. This paper develops and applies the Dagum model, demonstrating its advantages over other distributions such as the Log-Normal and Gamma distributions. The model's parameters are estimated using Maximum Likelihood Estimation (MLE) and the Method of Moments (MoM). A simulation study evaluates both methods' performance across various sample sizes, showing that MoM tends to offer more robust and precise estimates, particularly in small samples. These findings provide valuable insights into the ana