Comment on identification and estimation of nonlinear models using two samples with nonclassical measurement errors
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Publication:3569209
DOI10.1080/10485250903197212zbMath1334.62104OpenAlexW2038483121MaRDI QIDQ3569209
Publication date: 18 June 2010
Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/10485250903197212
Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) General nonlinear regression (62J02) Monte Carlo methods (65C05)
Cites Work
- New \(M\)-estimators in semi-parametric regression with errors in variables
- Deconvolution with unknown error distribution
- Adaptive density deconvolution with dependent inputs
- Efficient estimation in the errors in variables model
- Estimating a real parameter in a class of semiparametric models
- Convergence rate of sieve estimates
- Maximum likelihood estimation under a spatial sampling scheme
- On methods of sieves and penalization
- Adaptive wavelet estimator for nonparametric density deconvolution
- Minimax estimation of the noise level and of the deconvolution density in a semiparametric convolution model
- Likelihood inference in the errors-in-variables model
- Semi-parametric estimation in the nonlinear structural errors-in-variables model
- Adaptivity in convolution models with partially known noise distribution
- Penalized contrast estimator for adaptive density deconvolution
- Sharp Optimality in Density Deconvolution with Dominating Bias. II
- The ultrastructural relation: A synthesis of the functional and structural relations
- Data-driven deconvolution
- On the effect of estimating the error density in nonparametric deconvolution
- Deconvolution of supersmooth densities with smooth noise
- Finite sample penalization in adaptive density deconvolution
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