Statistical inference for semiparametric varying-coefficient partially linear models with error-prone linear covariates
114 citations recorded by the source corpus.
We study semiparametric varying-coefficient partially linear models when some linear covariates are not observed, but ancillary variables are available. Semiparametric profile least-square-based estimation procedures are developed for parametric and nonparametric components after we calibrate the error-prone covariates. Asymptotic properties of the proposed estimators are established. We also propose the profile least-square-based ratio test and Wald test to identify significant parametric and nonparametric components. To improve accuracy of the proposed tests for small or moderate sample sizes, Wild bootstrap version is also proposed to calculate the critical values. Intensive simulation experiments are conducted to illustrate the proposed approaches.
Subfields: Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/a36409c5d4799e46d2778e4300210dcdcd3beb45
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In Statistics and Probability
- Eun Ryung Lee
- Young K. Lee
- Byeong U. Park
- Enno Mammen
- James W. Taylor
- Juan-Carlos Escanciano
- Dimitris Nicolas Politis
- Lijian Yang
Citations in the corpus
- Varying Coefficient Regression Models: A Review and New Developments (2015, background): “You & Chen (2006) and Zhou & Liang (2009) extended the work of Fan & Huang (2005) to the case where all or some of the linear covariates U ij are subject to error.”