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Rate acceleration for estimators of integral curves from diffusion tensor imaging (DTI) data

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  • Sakhanenko, Lyudmila

Abstract

The components of a tensor field M are coefficients in a certain system of regression equations. To estimate the integral curve starting at a given point and driven by the principle eigenvector field corresponding to M, recently an estimation procedure has been constructed in Carmichael and Sakhanenko (2014). We propose a acceleration to this method which dampens the asymptotic mean integrated squared error and boosts the convergence rates. Supporting theoretical results and simulations are presented.

Suggested Citation

  • Sakhanenko, Lyudmila, 2015. "Rate acceleration for estimators of integral curves from diffusion tensor imaging (DTI) data," Statistics & Probability Letters, Elsevier, vol. 107(C), pages 286-295.
    • Handle: RePEc:eee:stapro:v:107:y:2015:i:c:p:286-295
    DOI: 10.1016/j.spl.2015.09.005
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    References listed on IDEAS

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    1. Zhu, Hongtu & Zhang, Heping & Ibrahim, Joseph G. & Peterson, Bradley S., 2007. "Statistical Analysis of Diffusion Tensors in Diffusion-Weighted Magnetic Resonance Imaging Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1085-1102, December.
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    Cited by:

    1. Sakhanenko, Lyudmila, 2017. "In search of an optimal kernel for a bias correction method for density estimators," Statistics & Probability Letters, Elsevier, vol. 122(C), pages 42-50.

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