Multivariate regression estimation with errors-in-variables for stationary processes

DOI10.1080/10485259308832569zbMath1380.62133OpenAlexW2030687190MaRDI QIDQ3432363

Elias Masry

Publication date: 16 April 2007

Published in: Journal of Nonparametric Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10485259308832569

Mathematics Subject Classification ID

Asymptotic properties of nonparametric inference (62G20) Nonparametric estimation (62G05) General nonlinear regression (62J02)

Related Items (13)

Nonparametric Estimation of the Conditional Mode with Errors-In-Variables: Strong Consistency for Mixing Processes ⋮ Estimating multivariate density and its derivatives for mixed measurement error data ⋮ Nonparametric regression with errors in variables and applications ⋮ Kernel regression estimation with errors-in-variables for random fields ⋮ Relative error prediction in nonparametric deconvolution regression model ⋮ Local polynomial fitting under association ⋮ Nonparametric estimation in time series with measurement errors ⋮ Nonparametric estimation in econometrics ⋮ Regression estimation under strong mixing data ⋮ Kernel estimates of the mean and the volatility functions in a nonlinear autoregressive model with ARCH errors ⋮ Linear least squares estimation of regression models for two-dimensional random fields ⋮ Multivariate partially linear regression in the presence of measurement error ⋮ Wavelet-Based estimation of multivariate regression functions in besov spaces^*

Cites Work

• Unnamed Item
• Approximation theorems for strongly mixing random variables
• On the optimal rates of convergence for nonparametric deconvolution problems
• Nonparametric regression estimation under mixing conditions
• On the consistency and finite-sample properties of nonparametric kernel time series regression, autoregression and density estimators
• Strong uniform convergence rates in robust nonparametric time series analysis and prediction: Kernel regression estimation from dependent observations
• Multivariate regression estimation with errors-in-variables: Asymptotic normality for mixing processes
• Asymptotic normality for deconvolution estimators of multivariate densities of stationary processes
• Fourier methods for estimating mixing densities and distributions
• A CENTRAL LIMIT THEOREM AND A STRONG MIXING CONDITION
• NONPARAMETRIC ESTIMATORS FOR TIME SERIES
• Multivariate probability density deconvolution for stationary random processes
• A consistent nonparametric density estimator for the deconvolution problem
• Deconvolving kernel density estimators
• Binary Regression Using an Extended Beta-Binomial Distribution, With Discussion of Correlation Induced by Covariate Measurement Errors
• Approximations for Regression with Covariate Measurement Error
• Optimal Rates of Convergence for Deconvolving a Density
• Weak and strong uniform consistency of kernel regression estimates
• Joint Asymptotic Distribution of the Estimated Regression Function at a Finite Number of Distinct Points

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