Keywords: nonlinear regression, asymptotic regression, stopping critical level, seeds imbibition Introduction Biological phenomena can have a mathematically characterized behavior as a function of ...

OpenAI's latest flagship model, GPT-4o, might actually be regressing, diminishing its performance to that of its smaller variant GPT-40-mini.

The Bayesian regression model with weakly informative prior is the best-fitted model compared to the standard Ordinary Least Squares regression and other Bayesian regression models with shrinkage ...

Hidden process regression (HPR) is a relatively new solution to fitting time series data that undergo a regime change. Current research in HPR has concentrated entirely on its prediction capacity, and ...

ABSTRACT: The L1 regression is a robust alternative to the least squares regression whenever there are outliers in the values of the response variable, or the errors follow a long-tailed distribution.

Learn how to use the Bayesian information criterion (BIC) to compare linear regression models in AI and why it is useful for model selection.

This paper considers sparse Bayesian learning (SBL) in the linear regression model used in signal processing. An estimation performance of the method is analyzed in the asymptotic case where the ...

Without regularisation, logistic regression’s asymptotic nature would continue to drive loss towards 0 in large dimensions. As a result, to reduce model complexity, most logistic regression models ...

To remedy these issues, we propose two extensions of the recently developed asymptotic Model-based Constrained Optimization (MBCO) likelihood ratio test (LRT), a promising new model comparison method ...