IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v52y2007i1p211-220.html
   My bibliography  Save this article

Wavelet-based procedures for proteomic mass spectrometry data processing

Author

Listed:
  • Chen, Shuo
  • Hong, Don
  • Shyr, Yu

Abstract

No abstract is available for this item.

Suggested Citation

  • Chen, Shuo & Hong, Don & Shyr, Yu, 2007. "Wavelet-based procedures for proteomic mass spectrometry data processing," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 211-220, September.
  • Handle: RePEc:eee:csdana:v:52:y:2007:i:1:p:211-220
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(07)00057-6
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lavielle, Marc, 1999. "Detection of multiple changes in a sequence of dependent variables," Stochastic Processes and their Applications, Elsevier, vol. 83(1), pages 79-102, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pastor, Dominique, 2008. "A theoretical result for processing signals that have unknown distributions and priors in white Gaussian noise," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3167-3186, February.
    2. Reynès, Christelle & Sabatier, Robert & Molinari, Nicolas & Lehmann, Sylvain, 2008. "A new genetic algorithm in proteomics: Feature selection for SELDI-TOF data," Computational Statistics & Data Analysis, Elsevier, vol. 52(9), pages 4380-4394, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Roberts, Leigh, 2014. "Consistent estimation of breakpoints in time series, with application to wavelet analysis of Citigroup returns," Working Paper Series 18815, Victoria University of Wellington, School of Economics and Finance.
    2. Surgailis, Donatas & Teyssière, Gilles & Vaiciulis, Marijus, 2008. "The increment ratio statistic," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 510-541, March.
    3. Salvatore Fasola & Vito M. R. Muggeo & Helmut Küchenhoff, 2018. "A heuristic, iterative algorithm for change-point detection in abrupt change models," Computational Statistics, Springer, vol. 33(2), pages 997-1015, June.
    4. Shi, Xiaoping & Wu, Yuehua & Miao, Baiqi, 2009. "Strong convergence rate of estimators of change point and its application," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 990-998, February.
    5. Fryzlewicz, Piotr, 2020. "Detecting possibly frequent change-points: Wild Binary Segmentation 2 and steepest-drop model selection," LSE Research Online Documents on Economics 103430, London School of Economics and Political Science, LSE Library.
    6. Fontaine, Charles & Frostig, Ron D. & Ombao, Hernando, 2020. "Modeling non-linear spectral domain dependence using copulas with applications to rat local field potentials," Econometrics and Statistics, Elsevier, vol. 15(C), pages 85-103.
    7. Mondher Bellalah & Marc Lavielle, 2002. "A Decomposition of Empirical Distributions with Applications to the Valuation of Derivative Assets," Multinational Finance Journal, Multinational Finance Journal, vol. 6(2), pages 99-130, June.
    8. Gabriela Ciuperca, 2011. "Estimating nonlinear regression with and without change-points by the LAD method," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 63(4), pages 717-743, August.
    9. Pan, Jianmin & Chen, Jiahua, 2006. "Application of modified information criterion to multiple change point problems," Journal of Multivariate Analysis, Elsevier, vol. 97(10), pages 2221-2241, November.
    10. Erico N de Souza & Kristina Boerder & Stan Matwin & Boris Worm, 2016. "Improving Fishing Pattern Detection from Satellite AIS Using Data Mining and Machine Learning," PLOS ONE, Public Library of Science, vol. 11(7), pages 1-20, July.
    11. Siu-Tong Au & Rong Duan & Siamak Hesar & Wei Jiang, 2010. "A framework of irregularity enlightenment for data pre-processing in data mining," Annals of Operations Research, Springer, vol. 174(1), pages 47-66, February.
    12. Piet Jong & Sonia Mazzi, 2001. "Modeling and Smoothing Unequally Spaced Sequence Data," Statistical Inference for Stochastic Processes, Springer, vol. 4(1), pages 53-71, January.
    13. Petracchi, Cosimo, 2022. "The Mussa puzzle: A generalization," European Economic Review, Elsevier, vol. 149(C).
    14. Cosimo Petracchi, 2021. "The Mussa Puzzle: A Generalization," Working Papers 2021-001, Brown University, Department of Economics.
    15. Bouzebda, Salim & Ferfache, Anouar Abdeldjaoued, 2023. "Asymptotic properties of semiparametric M-estimators with multiple change points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 609(C).
    16. Tang, Huaizhi & Yun, Wenju & Liu, Wenping & Sang, Lingling, 2019. "Structural changes in the development of China’s farmland consolidation in 1998–2017: Changing ideas and future framework," Land Use Policy, Elsevier, vol. 89(C).
    17. Elena Andreou, 2004. "The Impact of Sampling Frequency and Volatility Estimators on Change-Point Tests," Journal of Financial Econometrics, Oxford University Press, vol. 2(2), pages 290-318.
    18. Kirman, Alan & Teyssiere, Gilles, 2005. "Testing for bubbles and change-points," Journal of Economic Dynamics and Control, Elsevier, vol. 29(4), pages 765-799, April.
    19. Jean-Marc Bardet & Imen Kammoun & Veronique Billat, 2012. "A new process for modeling heartbeat signals during exhaustive run with an adaptive estimator of its fractal parameters," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(6), pages 1331-1351, December.
    20. Davies, Laurie & Höhenrieder, Christian & Krämer, Walter, 2012. "Recursive computation of piecewise constant volatilities," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3623-3631.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:52:y:2007:i:1:p:211-220. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.