Title: A robust goodness-of-fit test for small area estimation
Authors: Mahmoud Torabi - University of Manitoba (Canada) [presenting]
Jiming Jiang - University of California-Davis (United States)
Abstract: A method, originally proposed by R. A. Fisher, is developed into a general procedure, called tailoring, for deriving goodness-of-fit tests that are guaranteed to have a chi-squared asymptotic null distribution. The method has a robustness feature that it works correctly in testing a certain aspect of the model while some other aspects of the model may be misspecified. We apply the method to small area estimation. A connection, and difference, to the existing specification test is discussed. We evaluate the tests' performance theoretically and empirically, and compare it with several existing methods. Our empirical results suggest that the proposed test is more accurate in size, and has either higher or similar power compared to the existing tests. The proposed test is also computationally less demanding than the specification test and other comparing methods. A real-data application is discussed.