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Nonparametric adaptive density estimation on random fields using wavelet method

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  • Li, Linyuan

Abstract

We consider non-linear wavelet-based estimators of density functions with stationary random fields, which are indexed by the integer lattice points in the N-dimensional Euclidean space and are assumed to satisfy some mixing conditions. We investigate their asymptotic rates of convergence based on thresholding of empirical wavelet coefficients and show that these estimators achieve nearly optimal convergence rates within a logarithmic term over a large range of Besov function classes Bp,qs. Therefore, wavelet estimators still achieve nearly optimal convergence rates for random fields and provide explicitly the extraordinary local adaptability.

Suggested Citation

  • Li, Linyuan, 2015. "Nonparametric adaptive density estimation on random fields using wavelet method," Statistics & Probability Letters, Elsevier, vol. 96(C), pages 346-355.
    • Handle: RePEc:eee:stapro:v:96:y:2015:i:c:p:346-355
    DOI: 10.1016/j.spl.2014.10.012
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    References listed on IDEAS

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    1. Li, Linyuan, 2008. "On the block thresholding wavelet estimators with censored data," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1518-1543, September.
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    5. Linyuan Li & Kewei Lu, 2013. "On rate-optimal nonparametric wavelet regression with long memory moving average errors," Statistical Inference for Stochastic Processes, Springer, vol. 16(2), pages 127-145, July.
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    7. Chicken, Eric & Cai, T. Tony, 2005. "Block thresholding for density estimation: local and global adaptivity," Journal of Multivariate Analysis, Elsevier, vol. 95(1), pages 76-106, July.
    8. Tran, L. T. & Yakowitz, S., 1993. "Nearest Neighbor Estimators for Random Fields," Journal of Multivariate Analysis, Elsevier, vol. 44(1), pages 23-46, January.
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    Cited by:

    1. Krebs, Johannes T.N., 2018. "Nonparametric density estimation for spatial data with wavelets," Journal of Multivariate Analysis, Elsevier, vol. 166(C), pages 300-319.

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