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Unbalanced Haar Technique for Nonparametric Function Estimation

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  • Fryzlewicz, Piotr

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  • Fryzlewicz, Piotr, 2007. "Unbalanced Haar Technique for Nonparametric Function Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 1318-1327, December.
    • Handle: RePEc:bes:jnlasa:v:102:y:2007:m:december:p:1318-1327
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    Cited by:

    1. Philip Preuss & Ruprecht Puchstein & Holger Dette, 2015. "Detection of Multiple Structural Breaks in Multivariate Time Series," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 654-668, June.
    2. Schroeder, Anna Louise & Fryzlewicz, Piotr, 2013. "Adaptive trend estimation in financial time series via multiscale change-point-induced basis recovery," LSE Research Online Documents on Economics 54934, London School of Economics and Political Science, LSE Library.
    3. Aurore Delaigle & Peter Hall & Tung Pham, 2019. "Clustering functional data into groups by using projections," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 81(2), pages 271-304, April.
    4. Timmermans, Catherine & Delsol, Laurent & von Sachs, Rainer, 2011. "Using Bagidis in nonparametric functional data analysis: predicting from curves with sharp local features," LIDAM Discussion Papers ISBA 2011020, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Timmermans, Catherine & Delsol, Laurent & von Sachs, Rainer, 2013. "Using Bagidis in nonparametric functional data analysis: Predicting from curves with sharp local features," Journal of Multivariate Analysis, Elsevier, vol. 115(C), pages 421-444.
    6. Chau, Van Vinh & von Sachs, Rainer, 2018. "Intrinsic wavelet regression for surfaces of Hermitian positive definite matrices," LIDAM Discussion Papers ISBA 2018025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    7. Fryzlewicz, Piotr & Ombao, Hernando, 2009. "Consistent classification of non-stationary time series using stochastic wavelet representations," LSE Research Online Documents on Economics 25162, London School of Economics and Political Science, LSE Library.
    8. McGinnity, K. & Varbanov, R. & Chicken, E., 2017. "Cross-validated wavelet block thresholding for non-Gaussian errors," Computational Statistics & Data Analysis, Elsevier, vol. 106(C), pages 127-137.
    9. Homesh Sayal & John A. D. Aston & Duncan Elliott & Hernando Ombao, 2017. "An introduction to applications of wavelet benchmarking with seasonal adjustment," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 180(3), pages 863-889, June.
    10. Chau, Joris & von Sachs, Rainer, 2022. "Time-varying spectral matrix estimation via intrinsic wavelet regression for surfaces of Hermitian positive definite matrices," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    11. Fryzlewicz, Piotr, 2014. "Wild binary segmentation for multiple change-point detection," LSE Research Online Documents on Economics 57146, London School of Economics and Political Science, LSE Library.
    12. Timmermans, Catherine & de Tullio, Pascal & Lambert, Vincent & Frederich, Michel & Rousseau, Rejane & von Sachs, Rainer, 2012. "Advantages of the Bagidis methodology for metabonomics analyses: application to a spectroscopic study of Age-related Macular Degeneration," LIDAM Discussion Papers ISBA 2012004, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).

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