Modelling of Regression with Non-Gaussian AR Errors

Open access 2026

A Data-Driven Shrinkage for Efficient Estimation in the Generalized Poisson Regression Model: Simulation and Application

Count data in practice rarely conform to the equidispersion assumption that the standard Poisson regression model imposes. When counts are overdispersed or underdispersed, the generalized Poisson regression model (GPRM) steps in as a more accommodating alternative, extending the classical framework to handle such varia...

F. Almulhim, A. Hammad, E. Hussam et al. · Revista Internacional de Mét... · 0 citations

Open access Sep 2026

Outlier Detection in Beta Autoregressive Moving Average Model

The beta autoregressive moving average (βARMA) models are a dynamic model based on beta regression, used to model time series that take values in the interval (0, 1) and exhibit serial dependence. However, the presence of outliers can affect parameter estimation and the quality of predictions. Effective detection of th...

Haddadou Kamilia, Atil Lynda, Fellag Hocine · Stats · 0 citations

Preprint Aug 2026

A Design Concept of Forecasting Software for Normalized Vector Autoregressions with Fat Tails and Stochastic Volatility

A suite of R packages for macroeconomic forecasting that leverages advanced Bayesian, structural, multivariate, dynamic, hierarchical, hierarchical, non-linear, and non-Gaussian models is presented, which enables both structural and predictive analyses.

Fei Shang, Xiao-Lei Wang, Tomasz Woźniak · 0 citations

Open access Sep 2026

Full Bayesian Analysis of ARX Models Under Scale-Mixtures of Normal Errors: An Application to Solar Radiation in Najran, Saudi Arabia

Autoregressive models with exogenous variables (ARX) constitute a fundamental family of time series tools with broad applicability across engineering, environmental science, and finance. A persistent limitation of standard Bayesian treatments is the Gaussian error assumption, which frequently proves inadequate when dea...

Ayman A. Amin, Shuhrah A. Alghamdi · Mathematics · 0 citations

Review Open access Oct 2026

A comprehensive review of regression models under heteroscedasticity: research review

Heteroscedasticity is a common problem in linear regression analysis that can reduce the efficiency of ordinary least squares estimates, distort standard errors, and lead to unreliable statistical inference. This study aims to systematize the main approaches to detecting and correcting heteroscedasticity and to compare...

H. Yaseen, M. Taher, Wisam Wadullah Saleem · Информатика. Экономика. Упра... · 0 citations

Open access Oct 2026

A new quantile regression model for bounded responses: mathematical derivation, estimation, and fitting bounded datasets

Bounded response variables, such as rates, proportions, and indices, frequently arise in the applied sciences and require regression models that respect the unit interval support, rather than relying on Gaussian regression. In this work, we propose a new regression model for continuous bounded data based on the unit...

O. Alqasem, A. Gemeay, D. Alnagar et al. · Boundary Value Problems · 0 citations