Deconvolution of pulse trains with the L 0 penalty

Analytica Chimica Acta

Volumn 705, Issue 1-2, 2011, Pages 218-226

  De Rooi, Johan ^a   Eilers, Paul ^a  

^a ERASMUS MEDICAL CENTER   (Netherlands)

Author keywords

Blind deconvolution; Ill conditioned; Penalized regression; Spectra; Spike trains

Indexed keywords

BLIND DECONVOLUTION; DATA SETS; ILL-CONDITIONED; INPUT SPIKE TRAIN; ITERATIVE-WEIGHTED; LINEAR INVERSE PROBLEMS; OUTPUT SIGNAL; PENALIZED REGRESSION; PULSE TRAIN; SPIKE TRAIN;

ATMOSPHERIC SPECTRA; CONVOLUTION; IMPULSE RESPONSE; REGRESSION ANALYSIS;

INVERSE PROBLEMS;

ALGORITHM; ARTICLE; CONTROLLED STUDY; DECONVOLUTION; DNA DETERMINATION; ELECTROPHORESIS; INTERMETHOD COMPARISON; MATHEMATICAL MODEL; PRIORITY JOURNAL; REGRESSION ANALYSIS; SIGNAL PROCESSING;

CHROMATOGRAPHY, GAS; HUMANS; LUTEINIZING HORMONE; MODELS, STATISTICAL; REGRESSION ANALYSIS; SEQUENCE ANALYSIS, DNA;

EID: 80053385614     PISSN: 00032670     EISSN: 18734324     Source Type: Journal    
DOI: 10.1016/j.aca.2011.05.030     Document Type: Article

References (27)

1. Bandzuch P., Morháč M., Krištiak J. Study of the van Cittert and gold iterative methods of deconvolution and their application in the deconvolution of experimental spectra of positron annihilation. Nuclear Instruments and Methods in Physics Research A 1997, 384:506-515.
2. Brown F.T. Engineering System Dynamics 2006, CRC Press, New York.
3. Cardot H., Koo J.-Y., Park H.J., Trubuil A. Boosting diracs for eletrophoresis. Journal of Computational and Graphical Statistics 2004, 13(3):659-673.
4. Chen S., Donoho D., Saunders M. Atomic decomposition by basis pursuit. SIAM Journal of Scientific Computing 1998, 20(1):33-61.
5. Coombes K.R., Fritsche H.A., Clarke C., Neng Chen J., Baggerly K.A., Morris J.S., Chun Xiao L., Hung M.-C., Kuerer H.M. Quality control and peak finding for proteomics data collected from nipple aspirate fluid by surface-enhanced laser desorption and ionization. Clinical Chemistry 2003, 49(10):1615-1623.
6. Coombes K.R., Tsavachidis S., Morris J.S., Baggerly K.A., Hung M.-C., Kuerer H.M. Improved peak detection and quantification of mass spectrometry data acquired from surface-enhanced laser desorption and ionization by denoising spectra with the undecimated discrete wavelet transform. Proteomics 2005, 5:4107-4117.
7. Du P., Kibbe W.A., Lin S.M. Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching. Bioinformatics 2006, 22(17):2059-2065.
8. Eilers P. Parametric time warping. Analytical Chemistry 2004, 76(2):404-411.
9. 1 penalized estimation in the cox proportional hazards model. Biometrical Journal 2009, 51:1-15.
10. Hoerl A.E., Kennard R.W. Ridge regression: biased estimation for non orthogonal problems. Technometrics 1970, 12:55-67.
11. Deconvolution of Images and Spectra 1996, Academic Press, San Diego. P.A. Jansson (Ed.).
12. Li L., Speed T. Parametric deconvolution of positive spike trains. The Annals of Statistics 2000, 28(5):1279-1301.
13. Li L., Speed T. Deconvolution of sparse positive spikes. Journal of Computational and Graphical Statistics 2004, 13(4):853-870.
14. Mariscotti M.A. A method for automatic identification of peaks in the presence of background and its application to spectrum analysis. Nuclear Instruments and Methods 1967, 50:309-320.
15. Meinshausen N., Bühlmann P. High dimensional graphs and variable selection with the lasso. The Annals of Statistics 2006, 34(3):1436-1462.
16. Morháč M. Multidimensional peak searching algorithm for low-statistics nuclear spectra. Nuclear Instruments and Methods in Physics Research A 2007, 581:821-830.
17. Morháč M. An algorithm for determination of peak regions and baseline elimination in spectroscopic data. Nuclear Instruments and Methods in Physics Research A 2009, 600:478-487.
18. Morháč M., Matoušek V. High-resolution boosted deconvolution of spectroscopic data. Journal of Computational and Applied Mathematics 2011, 235:16291640.
19. Osborne M.R., Presnell B., Turlach B. On the lasso and its dual. Journal of Computational and Graphical Statistics 2000, 9(2):319-337.
20. Ruckstuhl A., Jacobson M., Field R., Dodd J. Baseline subtraction using robust local regression estimation. Journal of Quantitative Spectroscopy and Radiative Transfer 2001, 68(2):179-193.
21. Ryan C., Clayton E., Griffin W., Sie S., Cousens D. Snip, a statistics-sensitive background treatment for the quantitative analysis of pixel spectra in geoscience applications. Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 1988, 34:396-402.
22. Silagadze Z.K. A new algorithm for automatic photopeak searches. Nuclear Instruments and Methods in Physics Research A 1996, 376:451-454.
23. Tibshirani R. Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society Series B 1996, 58(1):267-288.
24. Vardi Y., Lee D. From image deblurring to optimal investments-maximum likelihood solutions for positive linear inverse problems. Journal of the Royal Statistical Society B 1993, 55(3):569-612.
25. Vis D.J., Westerhuis J.A., Hoefsloot H.C.J., Pijl H., Roelfsema F., van der Greef J., Smilde A.K. Endocrine pulse identification using penalized methods and a minimum set of assumptions. American Journal of Physiology and Endocrinology Metabolism 2010, 298(2):E146-155.
26. Wang L., Zhu J. Image denoising via solution paths. Annals of Operations Research 2010, 174(1):3-17.
27. Yasui Y., Pepe M., Thompson M.L., Adam B.-L., Wright G.L., Qu Y., Potter J.D., Winget M., Thornquist M., Feng Z. A data analytic strategy for protein biomarker discovery: profiling of high-dimensional proteomic data for cancer detection. Biostatistics 2003, 4(3):449-463.