Nonparametric Approach

Nonparametric approaches in statistics and machine learning offer flexible methods for data analysis that avoid restrictive assumptions about data distributions. Current research focuses on developing and refining nonparametric models for tasks such as causal inference (e.g., using generalized ps-BART models), regression calibration (employing distribution-free methods), and probabilistic regression (combining coarse learners). These advancements improve the accuracy and robustness of estimations, particularly in complex, nonlinear scenarios, and provide valuable tools for diverse applications including anomaly detection, human body estimation, and time series analysis.