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In this paper, we propose a local linear estimator for the regression model $$Y=m(X)+\varepsilon $$ Y = m ( X ) + ε based on the reciprocal inverse Gaussian kernel when the design variable is supported on $$(0,\infty )$$ ( 0 , ∞ ) . The conditional mean-squared error of the proposed estimator is derived, and its asymptotic properties are thoroughly investigated, including the asymptotic...
This paper investigates the estimation in a class of single-index varying coefficient regression model when some covariates are contaminated with measurement errors. A bias-corrected least square procedure based on the observed data is proposed. By replacing the nonparametric single index part with a local linear approximation, an iterative algorithm for estimating the index parameter is proposed...
Local linear fitting has been widely used for estimating the univariate regression function, it has numerous fantastic properties like minimax efficiency and boundary correction. The asymmetric kernel functions match the support of the explanatory variables, and we discover the inverse Gaussian (IG) kernel function is identical to the normal kernel with variable bandwidth. Based on these, this paper...
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