There is no available Prais-Winsten algorithm for regression with AR(2) or higher order errors, and the one with AR(1 …, providing an accurate, computationally fast, and inexpensive generic zig-zag algorithm. …

target variable (steel price) movement. A generalized reduced gradient algorithm (GRG) method is employed to estimate the … comparisons are made. The study finds that the combining model formed with the gradient algorithm approach in which the weights … generated by a constrained generalized reduced gradient algorithm (GRG) method. …

Logistic regression is widely used in complex data analysis. When predictors are at individual level and the response … at aggregate level, logistic regression can be estimated using the Maximum Likelihood Estimation (MLE) method with the …-maximization (EM) algorithm to avoid the direct maximization of the complicated likelihood function. Simulation studies have been …

This article presents the Exponential-Generalized Inverse Gaussian regression model with varying dispersion and shape … parameters is achieved through a novel Expectation Maximization (EM)-type algorithm that is computationally tractable and is …

selection and confirmed by both forward and backward selection criteria in the OLS regression. Notably, the shrinkage methods …, Ridge and Lasso regressions, demonstrated superior performance compared to OLS and KNN, with the Ridge regression presenting … the smallest test mean square error (MSE) of 318.30. This finding establishes the Ridge regression as the best model for …

algorithms related to Poisson regression, the proposed algorithm is both implementable and enjoys an improved AUC (0.71). The …In automobile insurance, it is common to adopt a Poisson regression model to predict the number of claims as part of …. Finite mixture regression modeling of telematics data is challenging to implement since the huge number of covariates …

incomplete information on covariates using the Weibull regression model. Model parameters were estimated using the expectation … maximization algorithm. The results of the data analysis and simulation demonstrated that the D&R approach is applicable for …

Cells (PEMFCs). The method uses a novel modified version of the Moth Search Algorithm, called Converged Moth Search … Algorithm (CMSA) to minimize the total of the squared deviations (TSD) between the output voltage and the experimental data. The …

Optimization Algorithm (MGRA) has been presented. Final results are compared with some several well-known algorithms to indicate …

improved fluid search optimization algorithm for optimal parameter identification of the undetermined parameters of the PEMFCs … algorithm is considered the cost function. Two empirical PEMFC models including BCS 500-W and NedStack PS6 are employed and … chaos-based fluid search optimization algorithm is successfully used to extract the parameters of a PEMFC model precisely. …