STATISTICAL DATA ANALYSIS

Nonparametric regression and ANOVA are important research directions in last 20 years with many applications. HUANG's research is developing new kinds of quantile, regression estimation, prediction and ANOVA methods. The mathematical properties of these estimators, predictors and test statistics are being studied: consistency, rate of convergence, efficiencies. She is building stochastic models based on these methods and applying them to economics, sciences, quality control, telecommunication network and biostatistics.

Studies of truncated and censored data have important applications in biostatistics, health studies, industrial engineering and other fields. Her research will develop new nonparametric, Bayesian and likelihood methods appropriate for such data. The work also links to combinatorial occupancy models and theory.

The above research utilizes computer simulations, bootstrap re-sampling in large data bases, and spectral analysis of time series.

In the traditional research paradigm, construction of an analytical model and the experimental effort that produces measurements for the same process are kept deliberately and scrupulously separate. The intent of this self-imposed stricture is to avoid any possible "tweeking" of the model to improve the agreement. STERNIN has proposed a very different research paradigm which deliberately makes analysis a part of the experiment. The model itself becomes a "parameter", subject to computer-based tweaking and straining. This yields estimates of the parameters, quantitative analysis of the misfit of the model to the experimental data, and, ultimately, better models. This methodology may be characterized as inverse theory algorithms.