Nonparametric and Semiparametric Modeling
In contrast to parametric modeling, where the distribution of the data is assumed
known up to a finite-dimensional parameter, nonparametric methods involve an infinite
dimensional parameter. Nonparametric methods are widely used in biomedical research.
For example, log rank tests and Kaplan-Meier estimates are standard tools in analyzing
censored survival data. A semiparametric model is intermediate between parametric
and nonparametric models and contains finite-dimensional and infinite-dimensional
parameters. For example, the widely used Cox model survival data is semiparametric.
Research in semiparametric models has been intense over the past two decades. In both
nonparametric and semiparametric modeling, empirical methods and smoothing are two
major ways to deal with the infinite-dimensional parameter. Faculty in biostatistics
are developing new methodologies and applying nonparametric and semiparametric techniques
in clinical trials, survival analyses, recurrent events, longitudinal studies, and
missing data problems.
Faculty: V. Baladandayuthapani, P. Han, K. He, N. Henderson, J. Kalbfleish, J. Kang, J. Morrison, P. Song, A. Tsodikov, L. Wang, Z. Wu, W. Ye, M. Zhang, L. Zhao