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This article is cited in 1 scientific paper (total in 1 paper)
Global rate optimality of integral curve estimators
in high order tensor models
C. Banerjeea, L. A. Sakhanenkoa, D. C. Zhub a Department of Statistics and Probability, Michigan State University, East Lansing, MI, USA
b Department of Radiology, Michigan State University, East Lansing, MI, USA
Abstract:
Motivated by an application in neuroimaging, we consider the problem of establishing global minimax lower bound in a high order tensor model. In particular, the methodology we describe provides the global minimax bound for the integral curve estimator proposed in [O. Carmichael and L. Sakhanenko, Linear Algebra Appl., 473 (2015), pp. 377–403]
under a semiparametric estimation setting. The theoretical results in this paper guarantee that the estimator used for tracing the complex fiber structure inside a live human brain obtained from high angular resolution diffusion imaging (HARDI) data is not only optimal locally but also optimal globally. The global minimax bound on the asymptotic risk of the estimators thus will provide a quantification of uncertainty for the estimation method in the whole domain of the imaging field. In addition to theoretical results, we also provide a detailed simulation study in order to find the optimal number of gradient directions for the imaging protocols, which we further illustrate with a real data analysis of a live human brain scan to showcase the uncertainty quantification of the estimation method in
[O. Carmichael and L. Sakhanenko, Linear Algebra Appl., 473 (2015), pp. 377–403]. Furthermore, based on the global minimax bound, we propose a method for comparing the relative accuracy of several commonly used neuroimaging protocols in diffusion tensor imaging (DTI).
Keywords:
global minimax lower bound, semiparametric estimation, magnetic resonance high angular resolution diffusion imaging.
Received: 18.10.2021 Revised: 01.11.2021 Accepted: 30.03.2022
Citation:
C. Banerjee, L. A. Sakhanenko, D. C. Zhu, “Global rate optimality of integral curve estimators
in high order tensor models”, Teor. Veroyatnost. i Primenen., 68:2 (2023), 301–321; Theory Probab. Appl., 68:2 (2023), 250–266
Linking options:
https://www.mathnet.ru/eng/tvp5534https://doi.org/10.4213/tvp5534 https://www.mathnet.ru/eng/tvp/v68/i2/p301
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Abstract page: | 138 | Full-text PDF : | 11 | References: | 33 | First page: | 4 |
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