Zero Inflated

Zero-inflated data, characterized by an excess of zero values compared to standard distributions, presents a significant challenge across diverse fields, from crime prediction to insurance claims modeling. Current research focuses on developing robust statistical models, such as zero-inflated Poisson and negative binomial regressions, often integrated with machine learning techniques like gradient boosting and Bayesian methods, including Thompson sampling and Bayesian CART, to accurately capture the data's unique structure. These advancements improve prediction accuracy and uncertainty quantification, leading to more reliable insights and better decision-making in various applications, including personalized healthcare interventions and resource allocation. The development of efficient algorithms for handling zero-inflated data is a key area of ongoing investigation.