Xianzheng
Huang
Department
of Statistics


Key phrases: Measurement error; Latent variables;
Model misspecification; Nonparametric statistics
Data prone to measurement error arise in
a wide range of applications. My longstanding research endeavor is studying
effects of measurement error on statistical inference. This line of
investigation motivates new strategies for drawing inference that adequately
account for measurement error.
Measurement error models fall under the
big umbrella of latent variables models, which are especially susceptible to
model misspecification. I have studied properties of inference results
associated with latent variable models in the presence of model
misspecification. Findings here lead to diagnostic methods for detecting
inadequate model assumptions.
The vulnerability of parametric
inference to model misspecification motivates my interests in nonparametric
statistics. In particular, I am interested in developing nonparametric methods
for mean regression, mode regression, and density estimation in the presence or
absence of measurement error.
Teaching
Advanced Statistical Inference; Latent
Variable Models; Linear Statistical Models; Nonlinear Statistical Models;
Mathematical Statistics (I & II); Introduction to Statistical Theory (I
& II, for Distance Learning); Statistical Methods (I); Statistics for
Engineers; Elementary Statistics.