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pro vyhledávání: '"Benhaddou, Rida"'
Autor:
Benhaddou, Rida, Liu, Qing
We investigate the nonparametric bivariate additive regression estimation in the random design and long-memory errors and construct adaptive thresholding estimators based on wavelet series. The proposed approach achieves asymptotically near-optimal c
Externí odkaz:
http://arxiv.org/abs/2205.10886
Autor:
Benhaddou, Rida, Connell, Matthew
In this work, we delve into the nonparametric empirical Bayes theory and approximate the classical Bayes estimator by a truncation of the generalized Laguerre series and then estimate its coefficients by minimizing the prior risk of the estimator. Th
Externí odkaz:
http://arxiv.org/abs/2112.09050
Autor:
Benhaddou, Rida
We look into the nonparametric regression estimation with additive and multiplicative noise and construct adaptive thresholding estimators based on Laguerre series. The proposed approach achieves asymptotically near-optimal convergence rates when the
Externí odkaz:
http://arxiv.org/abs/2012.11809
Akademický článek
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Autor:
Benhaddou, Rida
Anisotropic functional deconvolution model is investigated in the bivariate case under long-memory errors when the design points $t_i$, $i=1, 2, \cdots, N$, and $x_l$, $l=1, 2, \cdots, M$, are irregular and follow known densities $h_1$, $h_2$, respec
Externí odkaz:
http://arxiv.org/abs/1912.00478
Autor:
Benhaddou, Rida, Liu, Qing
We look into the minimax results for the anisotropic two-dimensional functional deconvolution model with the two-parameter fractional Gaussian noise. We derive the lower bounds for the $L^p$-risk, $1 \leq p < \infty$, and taking advantage of the Ries
Externí odkaz:
http://arxiv.org/abs/1812.07479
Autor:
Benhaddou, Rida, Liu, Qing
In the present paper, we consider the estimation of a periodic two-dimensional function $f(\cdot,\cdot)$ based on observations from its noisy convolution, and convolution kernel $g(\cdot,\cdot)$ unknown. We derive the minimax lower bounds for the mea
Externí odkaz:
http://arxiv.org/abs/1811.10411
Autor:
Benhaddou, Rida
We construct an adaptive wavelet estimator that attains minimax near-optimal rates in a wide range of Besov balls. The convergence rates are affected only by the weakest dependence amongst the channels, and take into account both noise sources.
Externí odkaz:
http://arxiv.org/abs/1806.00558
Autor:
Benhaddou, Rida
We investigate minimax results for the anisotropic functional deconvolution model when observations are affected by the presence of long-memory. Under specific conditions about the covariance matrices of the errors, we follow a standard procedure to
Externí odkaz:
http://arxiv.org/abs/1709.07022
Autor:
Benhaddou, Rida
We investigate the problem of estimating a function $f$ based on observations from its noisy convolution when the noise exhibits long-range dependence. We construct an adaptive estimator based on the kernel method, derive minimax lower bound for the
Externí odkaz:
http://arxiv.org/abs/1706.08648