Nonparametric Inference
Nonparametric inference refers to statistical techniques that use data to infer unknown quantities of interest while making as few assumptions as possible. This can involve working with large and flexible (potentially infinite-dimensional) statistical models, or assuming little about the data-generating process. The flexibility and adaptivity provided by nonparametric techniques is especially valuable in modern statistical problems of the current era of massive datasets and black-box AI.
Berkeley Statistics faculty work on many aspects of nonparametric inference. Current research interests include nonparametric hypothesis testing based on permutations, time-uniform confidence bounds, nonparametric regression, classification and density estimation under smoothness and shape constraints, black-box uncertainty quantification, Bayesian nonparametrics and neural modeling, as well as applications: in particular, to biological research, epidemic forecasting, election auditing, and racial justice in the legal system.
Researchers
statistics, machine learning, semiparametric models, asymptotic theory, hidden Markov models, applications to molecular biology
causal inference in experiments and observational studies, with applications to biomedical and social sciences;
contaminated data including missing data, measurement error, and selection bias
statistics, applied statistics, data science, statistical computing, computational biology and genomics
nonparametric and high-dimensional statistics, shape constrained statistical estimation, empirical processes, statistical information theory
artificial intelligence, control and intelligent systems and robotics, communications and networking
computer science, artificial intelligence, computational biology, statistics, machine learning, electrical engineering, applied statistics, optimization
machine learning, statistical prediction, variational inference, statistical computing, optimization
data science, statistics, machine learning
uncertainty quantification and inference, inverse problems, nonparametrics, risk assessment, earthquake prediction, election auditing, geomagnetism, cosmology, litigation, food/nutrition
high-dimensional statistics, nonparametric estimation, distribution-free inference, probabilistic forecasting, machine learning, convex optimization, numerical methods, tracking and forecasting epidemics
Mathematical Statistics, Applied Probability, and Statistical Learning Theory