Journal of the American Statistical Association· 2013Q1
Tensor Regression with Applications in Neuroimaging Data Analysis
- 569citations
- Q1SCImago
- 2013year
Short summary
A new family of tensor regression models is proposed to efficiently analyze ultrahigh-dimensional neuroimaging data, reducing dimensionality and enabling efficient estimation and prediction.
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Key points
- Introduces a new family of tensor regression models for complex data structures.
- Efficiently handles ultrahigh dimensionality in tensor covariates.
- Enables efficient estimation and prediction for neuroimaging data.
- Validated on synthetic and real MRI imaging data.
AI-generated from the title and abstract; the full text is not read.
Abstract
Classical regression methods treat covariates as a vector and estimate a corresponding vector of regression coefficients. Modern applications in medical imaging generate covariates of more complex form such as multidimensional arrays (tensors). Traditional statistical and computational methods are proving insufficient for analysis of these high-throughput data due to their ultrahigh dimensionality as well as complex structure. In this article, we propose a new family of tensor regression models that efficiently exploit the special structure of tensor covariates. Under this framework, ultrahigh dimensionality is reduced to a manageable level, resulting in efficient estimation and prediction. A fast and highly scalable estimation algorithm is proposed for maximum likelihood estimation and its associated asymptotic properties are studied. Effectiveness of the new methods is demonstrated on both synthetic and real MRI imaging data.
The authors' abstract, as published at the source. Journal of the American Statistical Association, 2013 · DOI ↗
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Field: Computational Mathematics
Computational MathematicsMathematics