Linear regression remains a cornerstone of statistical analysis, offering a framework for modelling relationships between a dependent variable and one or more independent predictors. Over the past ...

Fuzzy regression models extend traditional statistical regression by integrating fuzzy set theory to better handle imprecision and uncertainty inherent in many real-world data sets. These models ...

Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and ...

Many response variables are handled poorly by regression models when the errors are assumed to be normally distributed. For example, modeling the state damaged/not damaged of cells after treated with ...

Abstract: Assumptions play a pivotal role in the selection and efficacy of statistical models, as unmet assumptions can lead to flawed conclusions and impact decision ...

We study semiparametric varying-coefficient partially linear models when some linear covariates are not observed, but ancillary variables are available. Semiparametric profile least-square based ...

During the course of operation, businesses accumulate all kinds of data such as numbers related to sales performance and profit, and information about clients. Companies often seek out employees with ...