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Data dependent wavelet thresholding in nonparametric regression with change-point applications

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  • Ogden, Todd
  • Parzen, Emanuel

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  • Ogden, Todd & Parzen, Emanuel, 1996. "Data dependent wavelet thresholding in nonparametric regression with change-point applications," Computational Statistics & Data Analysis, Elsevier, vol. 22(1), pages 53-70, June.
    • Handle: RePEc:eee:csdana:v:22:y:1996:i:1:p:53-70
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    Cited by:

    1. Natalie Neumeyer & Ingrid Van Keilegom, 2009. "Change‐Point Tests for the Error Distribution in Non‐parametric Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 518-541, September.
    2. Beibei Guo & Yuehua Wu & Hong Xie & Baiqi Miao, 2011. "A segmented regime-switching model with its application to stock market indices," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(10), pages 2241-2252.
    3. Luan, Yihui & Xie, Zhongjie, 2001. "The wavelet identification for jump points of derivative in regression model," Statistics & Probability Letters, Elsevier, vol. 53(2), pages 167-180, June.
    4. Youssef Taleb & Edward A. K. Cohen, 2021. "Multiresolution analysis of point processes and statistical thresholding for Haar wavelet-based intensity estimation," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(2), pages 395-423, April.
    5. Haven, Emmanuel & Liu, Xiaoquan & Shen, Liya, 2012. "De-noising option prices with the wavelet method," European Journal of Operational Research, Elsevier, vol. 222(1), pages 104-112.
    6. Helena Bereś & Krzysztof Bereś & Jolanta Zięba, 2009. "Kurs złotego w świetle analizy falkowej," Gospodarka Narodowa. The Polish Journal of Economics, Warsaw School of Economics, issue 3, pages 61-88.
    7. Hotz, Thomas & Marnitz, Philipp & Stichtenoth, Rahel & Davies, Laurie & Kabluchko, Zakhar & Munk, Axel, 2012. "Locally adaptive image denoising by a statistical multiresolution criterion," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 543-558.

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