Multi-index Binary Response Analysis of Large Datasets
Journal of Business & Economic Statistics, 2010
Professor Prasad Naik and co-authors Michel Wedel from the University of Maryland and Wagner Kamakura from Duke University propose a multi-index binary response model for analyzing large databases (i.e., with many regressors). They combine many regressors into factors (or indexes) and then estimate the link function via parametric or nonparametric methods.
Neither the estimation of factors nor the determination of the number of factors requires ex ante knowledge of the link between the response and regressors. Furthermore, applying perturbation theory, we furnish a new asymptotic result to facilitate significance tests of factor loadings. We illustrate this approach with an empirical application in which we reduced dimensionality from 124 regressors to 4 factors.