Generalized concordance measure: Generalized regression model and dimension reduction

4^th International Conference and Exhibition on Biometrics & Biostatistics

November 16-18, 2015 San Antonio, USA

Wang Shao Hsuan

National Taiwan University, Taiwan

Scientific Tracks Abstracts: J Appl Computat Math

Abstract :

In the scientific research literature, rank-based measures have been widely used to characterize a monotonic association between a univariate response and some transformation of multiple covariates of interest. Instead of using a linear combination of covariates, we introduce a multivariate polynomial score to compute the corresponding concordance index through more general semi-parametric regression models. It involves the estimation for the degree of the multivariate polynomial and the central subspace (CS). To deal with this research issue, we propose a BIC-type estimation approach, which is implemented by an effective computational algorithm, to achieve the model selection consistency.

Biography :

Wang Shao Hsuan is currently studying at National Taiwan University, Department of Mathematics in a PhD program. He had published a paper in SCI while pursuing his Master’s degree.

Email: warmsay@yahoo.com.tw