Semi-Parametric Polynomial Inverse Regression for Dimension Reduction and Its Application in Microarray Data

As the development of the gene microarray technology, the contradiction between number of genes and sample size has become more apparent, high-dimension independent variables also challenge the traditional nonparametric methods. A new method for dimension reduction, semi-parametric polynomial inverse regression (SPPIR), based on sliced inverse regression is proposed. By simulation, we demonstrate how SPPIR can reduce the dimension of the input variables effectively. In the end, we conduct SPPIR and discriminant analysis for a tumor gene microarray data. By comparing with other methods, the effectiveness of dimension reduction methods can be seen.