Model checking for general linear error-in-covariables model with validation data |
| |
Authors: | Pengjie Dai Zhihua Sun Peng Wang |
| |
Institution: | (1) Computer Laboratory, Cambridge University, William Gates Building,15 JJ Thomson Avenue, Cambridge, CB3 0FD, UK;(2) Computer Science Department, University of Pisa, Largo Bruno Pontecorvo, 3, Pisa, 56127, ITALY |
| |
Abstract: | In this paper, model checking problem is considered for general linear model when covariables are measured with error and
an independent validation data set is available. Without assuming any error model structure between the true variable and
the surrogate variable, the author first apply nonparametric method to model the relationship between the true variable and
the surrogate variable with the help of the validation sample. Then the author construct a score-type test statistic through
model adjustment. The large sample behaviors of the score-type test statistic are investigated. It is shown that the test
is consistent and can detect the alternative hypothesis close to the null hypothesis at the rate n
−r with 0 ≤ r ≤ 1/2. Simulation results indicate that the proposed method works well. |
| |
Keywords: | |
本文献已被 SpringerLink 等数据库收录! |
|