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.

Regression models using fractional polynomials of the covariates have appeared in the literature in an ad hoc fashion over a long period; we provide a unified description and a degree of formalization ...

The simplest form of regression in Python is, well, simple linear regression. With simple linear regression, you're trying to ...

Learn how ARIMA models use time series data for accurate short-term forecasting. Discover its pros, cons, and essential tips ...

Model-combining (i.e., mixing) methods have been proposed in recent years to deal with uncertainty in model selection. Even though advantages of model combining over model selection have been ...

Logistic regression is a statistical method used to examine the relationship between a binary outcome variable and one or more explanatory variables. It is a special case of a regression model that ...

This article ( original research paper) proposes a systematic regression-based fundamental equity valuation model that can potentially be applied in areas such as quantitative finance and machine ...

Analysts debate XRP’s $200 target. Regression models show statistical potential, but on-chain data, whale moves, market maturity point to $3–$27 instead.