Tobit Model

Tobit models are statistical tools designed to analyze data where some observations are censored, meaning their true values are unknown but known to fall below or above a certain threshold. Current research focuses on extending the basic Tobit model to handle multiple censored variables simultaneously and on developing more efficient algorithms for parameter estimation, such as those leveraging sketching and coreset techniques for large datasets. These advancements improve the accuracy and scalability of Tobit models, enhancing their applicability in diverse fields, including environmental monitoring (e.g., water quality analysis) where censored data are common. The improved efficiency and flexibility of these models allow for more robust and reliable analyses of censored data across various scientific disciplines.