Department of Mathematics
Bias-corrected smoothed score function for single-index models
We in this paper investigate smoothed score function based confidence regions for parameters in single-index models. Because a plug-in estimator of nonparametric link function causes the bias of smoothed score function to be non-negligible, the limit of the score function is asymptotically normal with a non-zero mean due to the slow convergence rate of nonparametric estimation. A bias-corrected smoothed score function is recommended for achieving centered normal limit without under-smoothing or high order kernel, and then the confidence region can be constructed by chi-square distribution. Simulation studies are carried out to assess the performance of bias-corrected local likelihood, and to compare with normal approximation approach. © Springer-Verlag 2008.
Confidence region, Local likelihood, Single-index model, Smoothing score
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Link to Publisher's Edition
Chen, Q., Lin, L., & Zhu, L. (2010). Bias-corrected smoothed score function for single-index models. Metrika, 71 (1), 45-48. https://doi.org/10.1007/s00184-008-0201-8