Display options
Cite
Kachouie NN, Lin X, Schwartzman A. FDR control of detected regions by multiscale matched filtering. Commun Stat Simul Comput. 2014;46(1):127-144doi: 10.1080/03610918.2014.957842.
Kachouie, N. N., Lin, X., & Schwartzman, A. (2017). FDR control of detected regions by multiscale matched filtering. Communications in statistics: Simulation and computation, 46(1), 127-144. https://doi.org/10.1080/03610918.2014.957842
Kachouie, Nezamoddin N, et al. "FDR control of detected regions by multiscale matched filtering." Communications in statistics: Simulation and computation vol. 46,1 (2017): 127-144. doi: https://doi.org/10.1080/03610918.2014.957842
Kachouie NN, Lin X, Schwartzman A. FDR control of detected regions by multiscale matched filtering. Commun Stat Simul Comput. 2017;46(1):127-144. doi: 10.1080/03610918.2014.957842. Epub 2014 Dec 23. PMID: 31501637; PMCID: PMC6733272.
Copy Download .nbib Format: NLM
Share it on

Commun Stat Simul Comput. 2017;46(1):127-144. doi: 10.1080/03610918.2014.957842. Epub 2014 Dec 23.

FDR control of detected regions by multiscale matched filtering.

Communications in statistics: Simulation and computation

Nezamoddin N Kachouie, Xihong Lin, Armin Schwartzman

Affiliations

  1. Department of Mathematical Sciences, Florida Institute of Technology, Melbourne, FL, USA.
  2. Department of Biostatistics, Harvard School of Public Health, Boston, MA, USA.
  3. Department of Statistics, North Carolina State University, Raleigh, NC, USA.

PMID: 31501637 PMCID: PMC6733272 DOI: 10.1080/03610918.2014.957842

Abstract

Feature extraction from observed noisy samples is a common important problem in statistics and engineering. This paper presents a novel general statistical approach to the region detection problem in long data sequences. The proposed technique is a multi-scale kernel regression in conjunction with statistical multiple testing for region detection while controlling the false discovery rate (FDR) and maximizing the signal to noise ratio (SNR) via matched filtering. This is achieved by considering a one-dimensional (1D) region detection problem as its equivalent 0D (zero dimensional) peak detection problem. The detection method does not require

Keywords: False discovery rate; Feature extraction; Kernel regression; Local polynomial regression; Matched filtering; Multiple testing; Region detection; Signal reconstruction

References

  1. Biostatistics. 2004 Oct;5(4):557-72 - PubMed
  2. Comput Biol Chem. 2005 Feb;29(1):47-54 - PubMed
  3. Biostatistics. 2005 Apr;6(2):211-26 - PubMed
  4. Bioinformatics. 2005 Jul 1;21(13):3034-42 - PubMed
  5. Bioinformatics. 2006 Sep 1;22(17):2059-65 - PubMed
  6. Electrophoresis. 2008 Nov;29(20):4215-25 - PubMed
  7. BMC Bioinformatics. 2009 Jan 30;10 Suppl 1:S67 - PubMed
  8. Bioinformatics. 2009 May 1;25(9):1137-44 - PubMed
  9. Bioinformatics. 2011 Jan 15;27(2):161-6 - PubMed
  10. Rev Sci Instrum. 2011 Jan;82(1):015105 - PubMed
  11. IEEE Trans Signal Process. 2010 Nov 1;58(11):5883-5894 - PubMed
  12. PLoS One. 2011;6(5):e20060 - PubMed
  13. Ann Stat. 2011 Dec 1;39(6):3290-3319 - PubMed
  14. Ann Appl Stat. 2013;7(1):471-494 - PubMed

Publication Types

Grant support