Estimation in a semiparametric partially linear errors in variables model with inverse Gaussian kernel
Abstract This paper proposes an estimation procedure for the partial linear regression models, when the explanatory variable of the non parametric component is supported on and the covariates in the parametric part are contaminated by the measurement errors. Bias corrected estimators for the regression parameters and the non parametric components are constructed when the model errors are homoscedastic and heteroscedastic. Under some regularity conditions, large sample properties, including the consistency and asymptotic normality of the proposed estimators, are discussed. Simulation studies and real data analysis are conducted to evaluate the finite sample performance of the proposed estimation procedure.
Communications in Statistics Theory and Methods