TY - JOUR
T1 - Data-Driven Model Checking for Errors-In-Variables Varying-Coefficient Models with Replicate Measurements
AU - Wang, Miaomiao
AU - Liu, Catherine Chunling
AU - Xie, Tianfa
AU - Sun, Zhihua
N1 - Funding Information:
The authors would like to thank Professor Park, the Associate Editor and two reviewers for their careful review and insightful comments that have led to significant improvement of this article. Liu’s research was partially supported General Research Funding ( GRF 15327216 ), Research Grant Council (RGC) , Hong Kong, and Hong Kong Polytechnic University Grant 4BCC0 . Xie’s research was supported by National Natural Science Foundation of China No. 11771032 and the Science and Technology Project of Beijing Municipal Education Commission No. KM201710005032 . Sun’s research was supported by the National Natural Science Foundation of China (Grant Nos. 11571340 , U1430103 ), the Open Project of Key Laboratory of Big Data Mining and Knowledge Management, CAS.
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2020/1
Y1 - 2020/1
N2 - In this work, the adequacy check of errors-in-variables varying-coefficient models is investigated when replicate measurements are available. Estimation using the naive method that ignores measurement errors is biased. After the calibration of the estimators of the regression coefficient functions, we construct an empirical-process-based test statistic by the attenuation of corrected residuals. The asymptotic properties of the test statistic under the null hypothesis, global and various local alternatives are established. Simulation studies and real data analyses reveal that the proposed test performs satisfactorily.
AB - In this work, the adequacy check of errors-in-variables varying-coefficient models is investigated when replicate measurements are available. Estimation using the naive method that ignores measurement errors is biased. After the calibration of the estimators of the regression coefficient functions, we construct an empirical-process-based test statistic by the attenuation of corrected residuals. The asymptotic properties of the test statistic under the null hypothesis, global and various local alternatives are established. Simulation studies and real data analyses reveal that the proposed test performs satisfactorily.
KW - Additive measurement error
KW - Empirical process
KW - Model check
KW - Replicate measurements
KW - Varying-coefficient models
UR - http://www.scopus.com/inward/record.url?scp=85068524116&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2019.06.003
DO - 10.1016/j.csda.2019.06.003
M3 - Journal article
SN - 0167-9473
VL - 141
SP - 12
EP - 27
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
ER -