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Título: | On the automatic selection of the tuning parameter appearing in certain families of goodness-of-fit tests | Autor: | Tenreiro, Carlos | Palavras-chave: | goodness-of-fit tests; data-based tuning parameter selection; calibration; normality tests; empirical characteristic function; exponentiality tests; empirical Laplace transform | Data: | 2019 | Editora: | Taylor and Francis | Título da revista, periódico, livro ou evento: | Journal of Statistical Computation and Simulation | Volume: | 89 | Número: | 10 | Resumo: | The situation, common in the current literature, is that of a whole family of location-scale/scale invariant test statistics, indexed by a parameter $\lambda\in\Lambda$, is available to test the goodness of fit of $F$, the underlying distribution function of a set of independent real-valued random variables, to a location-scale/scale family of distribution functions. The power properties of the tests associated with the different statistics usually depend on the parameter $\lambda$, called the ``tuning parameter'', which is the reason that its choice is crucial to obtain a performing test procedure. In this paper, we address the automatic selection of the tuning parameter when $\Lambda$ is finite, as well as the calibration of the associated goodness-of-fit test procedure. Examples of existing and new tuning parameter selectors are discussed, and the methodology presented of combining different test statistics into a single test procedure is applied to well known families of test statistics for normality and exponentiality. A simulation study is carried out to access the power of the different tests under consideration, and to compare them with the fixed tuning parameter procedure, usually recommended in the literature. | URI: | https://hdl.handle.net/10316/87197 | DOI: | 10.1080/00949655.2019.1598409 | Direitos: | embargoedAccess |
Aparece nas coleções: | I&D CMUC - Artigos em Revistas Internacionais |
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