Exploiting predictor domain information in sufficient dimension reduction

Analysis of high-dimensional data is becoming the norm in a variety of scientific studies and dimension reduction methods are widely employed. As the predictor domain knowledge is often available, it is useful to incorporate such domain information into dimension reduction and subsequent model formulation. Existing solutions such as simple average, principal components analysis and partial least squares cannot assure preservation of full regression information when reducing the dimension. In this article we investigate sufficient dimension reduction strategies that can retain full regression information meanwhile utilizing prior domain knowledge. Both simulations and a real data analysis demonstrate that the new methods are effective and often superior than the existing solutions.