Abstract: Many rivers in the Western U.S. suffer from high salinity content due to both natural and human-induced causes. Computer simulation models are often used to estimate future salinity levels ...

We propose a series of Bayesian nonparametric statistical models for community detection in graphs. We model the probability of the presence or absence of edges within the graph. Using these models, ...

Mitchell Grant is a self-taught investor with over 5 years of experience as a financial trader. He is a financial content strategist and creative content editor. Timothy Li is a consultant, accountant ...

Bayesian nonparametric mixture models represent a powerful statistical framework that extends traditional mixture modelling by allowing the number of mixture components to be inferred from the data ...

This is a preview. Log in through your library . Abstract Efficiency scores of firms are measured by their distance to an estimated production frontier. The economic literature proposes several ...

Nonparametric estimation and U-statistics have emerged as vital tools in modern statistical analysis, offering robust alternatives to traditional parametric methods. Nonparametric techniques bypass ...

Daily flow data for the Usk River in Wales was simulated using a nonparametric bootstrap procedure that captures autocorrelation called k-nearest neighbor (k-NN) (Lall and Sharma, 1996). The objective ...

Non-parametric tests are used when standard assumptions are not available. These tests don’t rely on distributions, often ...

The team developed a non-parametric, model-free approach to quantify memory in complex systems such as soil, oceans, and ...