title: Tensor sufficient dimension reduction.
abstract: Tensor is a multiway array. With the rapid development of science and technology in the past decades, large amount of tensor observations are routinely collected, processed, and stored in many scientific researches and commercial activities nowadays. The colorimetric sensor array (CSA) data is such an example. Driven by the need to address data analysis challenges that arise in CSA data, we propose a tensor dimension reduction model, a model assuming the nonlinear dependence between a response and a projection of all the tensor predictors. The tensor dimension reduction models are estimated in a sequential iterative fashion. The proposed method is applied to a CSA data collected for 150 pathogenic bacteria coming from 10 bacterial species and 14 bacteria from one control species. Empirical performance demonstrates that our proposed method can greatly improve the sensitivity and specificity of the CSA technique.
authors.affiliation: Department of Statistics, University of Georgia, Athens, GA, USA.
authors.forename: Wenxuan
authors.initials: W
authors.lastname: Zhong
authors.affiliation: Department of Statistics, University of Georgia, Athens, GA, USA.
authors.forename: Xin
authors.initials: X
authors.lastname: Xing
authors.affiliation: Department of Chemistry, University of Illinois, Urbana, IL, USA.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Suslick
keywords: dimension reduction; iterative estimation; sliced inverse regression; tensor analysis