1 citations · 1 across the 11 of their papers we have counts for
14 papers
Minimax estimation for Varying Coefficient Model via Laguerre Series
Rida Benhaddou, Khalid Chokri, Jackson Pinschenat
We delve into the estimation of the functional coefficients and inference for varying coefficient model. Applying Laguerre series, we develop an estimator for the vector of functio…
Adaptive estimation for the nonparametric bivariate additive model in random design with long-memory dependent errors
Rida Benhaddou, Qing Liu
We investigate the nonparametric bivariate additive regression estimation in the random design and long-memory errors and construct adaptive thresholding estimators based on wavele…
Nonparametric empirical Bayes estimation based on generalized Laguerre series
Rida Benhaddou, Matthew Connell
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 estim…
Estimation in nonparametric regression model with additive and multiplicative noise via Laguerre series
Rida Benhaddou
We look into the nonparametric regression estimation with additive and multiplicative noise and construct adaptive thresholding estimators based on Laguerre series. The proposed ap…
Anisotropic Functional Deconvolution for the irregular design with dependent long-memory errors
Rida Benhaddou
Anisotropic functional deconvolution model is investigated in the bivariate case under long-memory errors when the design points , , and , $l=1, 2, \cd…
Anisotropic functional deconvolution with long-memory noise: the case of a multi-parameter fractional Wiener sheet
Rida Benhaddou, Qing Liu
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…
Minimax adaptive wavelet estimator for the anisotropic functional deconvolution model with unknown kernel
Rida Benhaddou, Qing Liu
In the present paper, we consider the estimation of a periodic two-dimensional function based on observations from its noisy convolution, and convolution kernel $g…
Minimax adaptive wavelet estimator for the simultaneous blind deconvolution with fractional Gaussian noise
Rida Benhaddou
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…
Anisotropic functional Fourier deconvolution with long-memory dependent errors: a minimax study
Rida Benhaddou
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…
Laplace deconvolution in the presence of indirect long-memory data
Rida Benhaddou
We investigate the problem of estimating a function based on observations from its noisy convolution when the noise exhibits long-range dependence. We construct an adaptive est…
Anisotropic functional Laplace deconvolution
Rida Benhaddou, Marianna Pensky, Rasika Rajapakshage
In the present paper we consider the problem of estimating a three-dimensional function based on observations from its noisy Laplace convolution. Our study is motivated by the…
Multichannel Deconvolution with Long-Range Dependence: A Minimax Study
Rida Benhaddou, Rafal Kulik, Marianna Pensky +1
We consider the problem of estimating the unknown response function in the multichannel deconvolution model with long-range dependent Gaussian errors. We do not limit our considera…
Anisotropic Denoising in Functional Deconvolution Model with Dimension-free Convergence Rates
Rida Benhaddou, Marianna Pensky, Dominique Picard
In the present paper we consider the problem of estimating a periodic -dimensional function based on observations from its noisy convolution. We construct a wavelet esti…
Adaptive Nonparametric Empirical Bayes Estimation Via Wavelet Series: the Minimax Study
Rida Benhaddou, Marianna Pensky
In the present paper, we derive lower bounds for the risk of the nonparametric empirical Bayes estimators. In order to attain the optimal convergence rate, we propose generalizatio…