The problem of estimating from a sample a confidence region for the parameters of the distribution of maximum values is treated by setting up what are called "statistical estimation functions" ...

Modern statistical modelling is increasingly focused on reducing bias and enhancing the accuracy of parameter estimates. Traditional maximum likelihood estimation, while powerful, can encounter ...

This is a preview. Log in through your library . Abstract Correlation matrices play a key role in many multivariate methods (e.g., graphical model estimation and factor analysis). The current state-of ...

This repository contains code and analysis related to statistical inference and estimation techniques, focusing on confidence intervals, bootstrap methods, UMVUE, exponential families, and MLE ...

Abstract: We present a new uncertainty principle for risk-aware statistical estimation, effectively quantifying the inherent trade-off between mean squared error (mse ...

This project applies nonparametric statistical methods, including the Wilcoxon Rank-Sum Test, Kruskal-Wallis Test, bootstrapping, and Monte Carlo simulations, to analyze data without normality ...

In 2013 the European Chemical Agency (ECHA) proposed threshold limit values for exposure concentration of hexavalent chromium, CR(VI), at workplaces in Europe which serve as a (non-legally binding). 1 ...

Abstract: This paper presents a comparative study of statistical modeling and Artificial Intelligence (AI) approaches for estimating essential parameters of lithium-ion (Li-ion) batteries in electric ...