| 讲座简介： | For regression models, most of the existing model checking tests can be categorized into the broad class of local smoothing tests and of global smoothing tests.
Compared with global smoothing tests, local smoothing tests can only detect local
alternatives distinct from the null hypothesis at a much slower rate when the dimension of predictor vector is high, but can be more sensitive to oscillating alternatives.
In this paper, we suggest a projection-based test in multivariate scenarios to bridge
between the local and global smoothing-based methodologies such that the test can
benefit from the advantages of the two types of tests. The test construction rests on
a kernel-based method and the resulting test becomes a distance-based test with a
closed form. Wild bootstrap is applied to determine the critical values. Simulation
results show that the proposed test has better performance than some typical competitors in this area when dimension goes higher. A real data example is analyzed to
show its usefulness. |
