Factor Analysis Regression for Predictive Modeling with High-Dimensional Data

Factor analysis regression (FAR) of \(y _i\) on \({{\varvec{x}}}_i=(x _{1i},x _{2i},\ldots ,x _{pi})\), i = 1,2,...,n, has been studied only in the low-dimensional case \((p < n )\), using maximum likelihood (ML) factor extraction. The ML method breaks down in high-dimensional cases \((p >n )\). In this paper, we develop a high-dimensional version of FAR based on a computationally efficient method of factor extraction. We compare the performance of our high-dimensional FAR with partial least squares regression (PLSR) and principal component regression (PCR) under three underlying correlation structures: arbitrary correlation, factor model correlation structure, and when y is independent of x. Under each structure, we generated Monte Carlo training samples of sizes \(n <p\) from a multivariate normal distribution with each structure. Parameters were fixed at estimates obtained from analyses of real data sets. Given the independence structure, we observed severe over-fitting by PLSR compared to FAR and PCR. Under the two dependent structures, FAR had a notably better average mean square error of prediction than PCR. The performance of FAR and PLSR were not notably different given the dependent structures. Thus, overall, FAR performed better than either PLSR or PCR.