Partially linear multivariate regression in the presence of measurement error - (:unkn) unknown (KOREAN STATISTICAL SOC, 2020)
In this paper, a partially linear multivariate model with error in the explanatory variable of the nonparametric part, and an m dimensional response variable is considered. Using the uniform consistency results found for the estimator of the nonparametric part, we derive an estimator of the parametric part. The dependence of the convergence rates on the errors distributions is examined and demonstrated that proposed estimator is asymptotically normal. In main results, both ordinary and super smooth error distributions are considered. Moreover, the derived estimators are applied to the economic behaviors of consumers. Our method handles contaminated data is founded more effectively than the semiparametric method ignores measurement errors.
Keywords: multivariate regression; partially linear model; errors in variables; kernel smoothing; asymptotic normality; Engel curves; ASYMPTOTIC NORMALITY; IN-VARIABLES; NONPARAMETRIC REGRESSION; DECONVOLUTION; DENSITIES