Nonparametric and Semiparametric Modeling

Nonparametric & Semiparametric ModelingIn 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. WangZ. Wu, W. Ye, M. Zhang, L. Zhao

Links: Kidney Epidemiology & Cost Center