Robust and consistent estimation of nonlinear errors-in-variables models

By Tong Li. 2002

141 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4b4fc5f0301c11934389040b0cdc26a587ffcff7

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In Control and Systems Engineering

In Statistics and Probability

Citations in the corpus

Measurement Error Models with Auxiliary Data (2005, methodology): “The class of models that Carroll and Wand (1991), Sepanski and Carroll (1993) and Lee and Sepanski (1995) consider is smaller than the one we cover in this paper.”
Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution (2008, unknown): “Estimation of a general nonlinear model with measurement error in the covariates is a notoriously difficult problem that has received considerable attention in the recent econometrics literature (relevant studies using repeated measurements or instrumental variables include Hausman et al., 1991; Wan”
Semiparametric Efficiency in GMM Models of Nonclassical Measurement Errors, Missing Data and Treatment Effects (2008, unknown): “Recently, for estimation of the average treatment effect parameter or/and of the mean parameter in missing data models, Wang, Linton, and Hardle (2004) suggested that semiparametrically specified propensity score, such as a single index or a partially linear form, can be used to reduce the curse of ”
On deconvolution with repeated measurements (2008, unknown): “Recent, related research in the regression setting, and in the econometrics literature, includes that of Li (2002), Li and Hsiao (2004) and Schennach (2004a, 2004b), who demonstrated that replications can be used to good effect in regression problems with measurement error.”
Estimation of Nonlinear Models with Measurement Error Using Marginal Information1 (2004, unknown): “m(y, x, w, θ, fx, fx∗, fw|x) = R X∗ ∂f∗(y|x∗,w;θ) ∂θ g1(x− x)g2(x, w)dx∗ R X∗ f ∗(y|x∗, w; θ)g1(x− x∗)g2(x∗, w)dx∗ (49) with”
Nonparametric Regression Estimation in the Heteroscedastic Errors-in-Variables Problem (2007, background): “Recent related work in the econometrics literature includes that of Horowitz and Markatou (1996), Li (2002), Li and Hsiao (2004), and Schennach (2004a,b).”
Instrumental Variable Estimation of Nonlinear Errors-in-Variables Models (2004, methodology): “…the characteristic function φ (ζ) of the disturbance u to be nonvanishing everywhere is a standard assumption in the deconvolution literature (Carroll, Ruppert, and Stefanski (1995), Fan (1991), Fan and Truong (1993), Li and Vuong (1998), Li (2002), Horowitz and Markatou (1996), Schennach (2004a)).”
Generalized Non-Parametric Deconvolution with an Application to Earnings Dynamics (2010, background): “See Li (2002), Schennach (2004) and Hu and Ridder (2005). each individual in the sample given observables.”