Beta Regression Models: Joint Mean and Variance Modeling

Bayesian Beta Regression Models Joint Mean and Precision Modeling Edilberto Cepeda Cuervo

Regression Models with Heteroscedasticity using Bayesian Approach Modelos de regresión heterocedásticos usando aproximación bayesiana

In this paper, we compare the performance of two statistical approaches for the analysis of data obtained from the social research area. In the first approach, we use normal models with joint regression modelling for the mean and for the variance heterogeneity. In the second approach, we use hierarchical models. In the first case, individual and social variables are included in the regression m...

QSPR Analysis with Curvilinear Regression Modeling and Topological Indices

Topological indices are the real number of a molecular structure obtained via molecular graph G. Topological indices are used for QSPR, QSAR and structural design in chemistry, nanotechnology, and pharmacology. Moreover, physicochemical properties such as the boiling point, the enthalpy of vaporization, and stability can be estimated by QSAR/QSPR models. In this study, the QSPR (Quantitative St...

Mathematical Modeling and Analysis of Spark Erosion Machining Parameters of Hastelloy C-276 Using Multiple Regression Analysis (RESEARCH NOTE)

Electrical discharge machining has the capability of machining complicated shapes in electrically conductive materials independent of hardness of the work materials. This present article details the development of multiple regression models for envisaging the material removal rate and roughness of machined surface in electrical discharge machining of Hastelloy C276. The experimental runs are de...

Statistical Learning Tools for Heteroskedastic Data

Many regression procedures are affected by heteroskedasticity, or non-constant variance. A classic solution is to transform the response y and model h(y) instead. Common functions h require a direct relationship between the variance and the mean. Unless the transformation is known in advance, it can be found by applying a model for the variance to the squared residuals from a regression fit. Un...