Asymptotic optimality of new adaptive test in regression model. (English) Zbl 1098.62053
Summary: This paper presents some results on asymptotic optimality of the test procedure introduced recently by Y. Baraud, S. Huet and B. Laurent [Adaptive tests of linear hypotheses by model selection. Ann. Stat. 31, 225–251 (2003; Zbl 1018.62037)]. The optimality relies on some comparisons of the ability of the new statistic to discriminate between the null hypothesis and convergent alternatives with capability of the respective Neyman–Pearson test.
MSC:
62G10 | Nonparametric hypothesis testing |
62G20 | Asymptotic properties of nonparametric inference |
62G08 | Nonparametric regression and quantile regression |