Development and application of statistical methods for the social, medical, and biological sciences, including (i) the combination of evidence across studies to draw general conclusions (meta-analysis), (ii) the design and analysis of social experiments to inform public policy, and (iii) the role of uncertainty in cognitive science, trying to understand how human cognition is shaped by the fact that it involves processing of information from memory and perception that is inherently uncertain.
Likelihood inference, higher-order asymptotic theory, nonparametric and semiparametric methods, and statistical methods in finance.
Time series analysis, stochastic processes and their applications, robust statistics, extreme value theory, and financial mathematics. Recent work investigates model fitting and prediction for non-Gaussian and nonlinear time series processes, which has applications in economics, finance, geosciences, and signal processing.
Mathematical statistics; biostatistics; statistical and computational learning theory.
Production and use of public statistics, particularly in policy-laden contexts; sampling theory and practice; statistical measurement of system performance, such as accuracy of verdicts in criminal trials; statistical demography.
Mathematical probability (in particular, large deviation theory) and Bayesian statistics (especially the study of exchangeability); history, philosophical foundations, and legal, forensic, and medical applications of probability and statistics.
Statistics and biology, including mixture model, genomics and bioinformatics
Non-normal nonlinear time-series analysis, including threshold models and applications to plague dynamics.
Markov chain Monte Carlo methods for Bayesian and frequentist inference, nonparametric estimation of the hazard function for right-censored and interval-censored data, applications of multiple imputation to censored regression data, models and measures of interrater agreement/ disagreement, and mathematical models of carcinogenesis. Recent work considers the use of Bayesian inference in mixtures-of-experts and hierarchical mixtures-of-experts neural network architectures with applications to speech recognition, breast cancer diagnosis, financial exchange rate data, and global meteorological information.
Hongmei JiangMultiple testing, microarray data analysis, computational biology and bioinformatics. Back to top