A0404
Title: Improving tensor regression by optimal model averaging
Authors: Jiahui Zou - Capital University of Economics and Business (China) [presenting]
Abstract: Tensors have broad applications in neuroimaging, data mining, digital marketing, etc. CANDECOMP/PARAFAC (CP) tensor decomposition can effectively reduce the number of parameters to gain dimensionality-reduction and thus plays a key role in tensor regression. However, in CP decomposition, there is uncertainty about which rank to use. A model averaging method is developed to handle this uncertainty by weighting the estimators from candidate tensor regression models with different ranks. When all candidate models are misspecified, it is proved that the model averaging estimator is asymptotically optimal. When correct models are included in the set of candidate models, the consistency of parameters and the convergence of the model averaging weight are proven. Simulations and empirical studies illustrate that the proposed method has superiority over the competition methods and has promising applications.
