• Bernoulli
  • Volume 25, Number 1 (2019), 623-653.

Optimal rates of statistical seriation

Nicolas Flammarion, Cheng Mao, and Philippe Rigollet

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Given a matrix, the seriation problem consists in permuting its rows in such way that all its columns have the same shape, for example, they are monotone increasing. We propose a statistical approach to this problem where the matrix of interest is observed with noise and study the corresponding minimax rate of estimation of the matrices. Specifically, when the columns are either unimodal or monotone, we show that the least squares estimator is optimal up to logarithmic factors and adapts to matrices with a certain natural structure. Finally, we propose a computationally efficient estimator in the monotonic case and study its performance both theoretically and experimentally. Our work is at the intersection of shape constrained estimation and recent work that involves permutation learning, such as graph denoising and ranking.

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Bernoulli, Volume 25, Number 1 (2019), 623-653.

Received: January 2017
Revised: August 2017
First available in Project Euclid: 12 December 2018

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adaptation matrix estimation minimax estimation permutation learning shape constraints statistical seriation


Flammarion, Nicolas; Mao, Cheng; Rigollet, Philippe. Optimal rates of statistical seriation. Bernoulli 25 (2019), no. 1, 623--653. doi:10.3150/17-BEJ1000.

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Supplemental materials

  • Supplement to “Optimal Rates of Statistical Seriation”. We include additional technical details in this supplement.