Sparse signals estimation for adaptive sampling

  • Andrey Ordin

Abstract

This paper presents an estimation procedure for sparse signals in adaptive setting. We show that when the pure signal is strong enough, the value of loss function is asymptotically the same as for an optimal estimator up to a constant multiplier.

References

F. Abramovich, Y. Benjamini, D. L. Donoho, I. M. Johnstone. Adapting to unknown sparsity by controlling false discovery rate. The Annals of Statistics, 34, (2006), 584-653.

T. T. Cai, X. J. Jeng, J. Jin. Optimal detection of heterogeneous and heteroscedastic mixtures. Journal of the Royal Statistical Society: Series B, 73(5), (2011), 629-662.

J. Haupt, R. Castro, R. Nowak. Distilled Sensing: Selective Sampling for Sparse Signal Recovery, (2009), International Conference on Artificial Intelligence and Statistics.

A. O. Hero, III, D. Wei. Performance guarantees for adaptive estimation of sparse signals, (2013), arxiv preprint arXiv:1311.6360v1.

A. M. Hopkins. A new source detection algorithm using the false-discovery rate, Astron. J., 123, (2002), 1086-1094.

Y. Pawitan, S. Michiels, S. Koscielny, A. Gusnanto, A. Ploner. False discovery rate, sensitivity, and sample size for microarray studies. Bioinformatics, Vol. 21, (2005), 3017-3024.

J. L. Starck, F. Murtagh, J. M. Fadili. Sparse Image and Signal Processing, (2010), Cambridge University Press.

Published
2014-08-24
How to Cite
Ordin, A. (2014). Sparse signals estimation for adaptive sampling. Statistics, Optimization & Information Computing, 2(3), 234-242. https://doi.org/10.19139/soic.v2i3.71
Section
Research Articles