The Annals of Statistics

Multiscale blind source separation

Merle Behr, Chris Holmes, and Axel Munk

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Abstract

We provide a new methodology for statistical recovery of single linear mixtures of piecewise constant signals (sources) with unknown mixing weights and change points in a multiscale fashion. We show exact recovery within an $\varepsilon$-neighborhood of the mixture when the sources take only values in a known finite alphabet. Based on this we provide the SLAM (Separates Linear Alphabet Mixtures) estimators for the mixing weights and sources. For Gaussian error, we obtain uniform confidence sets and optimal rates (up to log-factors) for all quantities. SLAM is efficiently computed as a nonconvex optimization problem by a dynamic program tailored to the finite alphabet assumption. Its performance is investigated in a simulation study. Finally, it is applied to assign copy-number aberrations from genetic sequencing data to different clones and to estimate their proportions.

Article information

Source
Ann. Statist., Volume 46, Number 2 (2018), 711-744.

Dates
Received: July 2016
Revised: January 2017
First available in Project Euclid: 3 April 2018

Permanent link to this document
https://projecteuclid.org/euclid.aos/1522742434

Digital Object Identifier
doi:10.1214/17-AOS1565

Mathematical Reviews number (MathSciNet)
MR3782382

Zentralblatt MATH identifier
06870277

Subjects
Primary: 62G08: Nonparametric regression 62G15: Tolerance and confidence regions
Secondary: 92D10: Genetics {For genetic algebras, see 17D92}

Keywords
Multiscale inference honest confidence sets change point regression finite alphabet linear mixture exact recovery genetic sequencing

Citation

Behr, Merle; Holmes, Chris; Munk, Axel. Multiscale blind source separation. Ann. Statist. 46 (2018), no. 2, 711--744. doi:10.1214/17-AOS1565. https://projecteuclid.org/euclid.aos/1522742434


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Supplemental materials

  • Supplement to Multiscale Blind Source Separation. Proofs of Theorem 1.4, Theorem 2.5, and Theorem 2.7 (Section S1); additional details on algorithms (Section S2); additional figures and tables from Section 4 and 5 (Section S3); details on the SST-method (Section S4).