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Nonparametric inference with generalized likelihood ratio tests (With comments and rejoinder). (English) Zbl 1131.62035
Summary: The advance of technology facilitates the collection of statistical data. Flexible and refined statistical models are widely sought in a large array of statistical problems. The question arises frequently whether or not a family of parametric or nonparametric models fit adequately the given data. We give a selective overview on nonparametric inferences using generalized likelihood ratio (GLR) statistics. We introduce generalized likelihood ratio statistics to test various null hypotheses against nonparametric alternatives. The trade-off between the flexibility of alternative models and the power of the statistical tests is emphasized. Well-established Wilks’ phenomena are discussed for a variety of semi- and nonparametric models, which sheds light on other research using GLR tests. A number of open topics worthy of further study are given in a discussion section.

MSC:
62G10 Nonparametric hypothesis testing
62G09 Nonparametric statistical resampling methods
62G08 Nonparametric regression and quantile regression
Software:
gss; KernSmooth
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