TY - JOUR
T1 - A goodness-of-fit test for variable-adjusted models
AU - Xie, Chuanlong
AU - Zhu, Lixing
N1 - Funding Information:
Lixing Zhu is a chair professor of Department of Mathematics at Hong Kong Baptist University, Hong Kong, and a professor of School of Statistics at Beijing Normal University, Beijing, China. Lixing Zhu's research was supported by a grant from the University Grants Council of Hong Kong, Hong Kong, China and a grant from the Natural Science Foundation of China (11671042).
PY - 2019/10
Y1 - 2019/10
N2 - This research provides a projection-based test to check parametric single-index regression structure in variable-adjusted models. An adaptive-to-model strategy is employed, which makes the proposed test work better on the significance level maintenance and more powerful than existing tests. With mild conditions, the proposed test asymptotically behaves like a test that is for classical regression setup without distortion errors in observations. Numerical studies with simulated and real data are conducted to examine the performance of the test in finite sample scenarios.
AB - This research provides a projection-based test to check parametric single-index regression structure in variable-adjusted models. An adaptive-to-model strategy is employed, which makes the proposed test work better on the significance level maintenance and more powerful than existing tests. With mild conditions, the proposed test asymptotically behaves like a test that is for classical regression setup without distortion errors in observations. Numerical studies with simulated and real data are conducted to examine the performance of the test in finite sample scenarios.
KW - Adaptive-to-model test
KW - Dimension reduction
KW - Distortion errors
KW - Variable-adjusted model
UR - http://www.scopus.com/inward/record.url?scp=85063878560&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2019.01.018
DO - 10.1016/j.csda.2019.01.018
M3 - Journal article
AN - SCOPUS:85063878560
SN - 0167-9473
VL - 138
SP - 27
EP - 48
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
ER -