Robust testing for superiority between two regression curves
The problem of testing the null hypothesis that the regression functions of two populations are equal versus one-sided alternatives under a general nonparametric homoscedastic regression model is considered. To protect against atypical observations, the test statistic is based on the residuals obtai...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2016 |
| País: | Argentina |
| Institución: | Consejo Nacional de Investigaciones Científicas y Técnicas |
| Repositorio: | CONICET Digital (CONICET) |
| Idioma: | inglés |
| OAI Identifier: | oai:ri.conicet.gov.ar:11336/18946 |
| Acceso en línea: | http://hdl.handle.net/11336/18946 |
| Access Level: | acceso abierto |
| Palabra clave: | Hypothesis Testing Nonparametric Regression Models Robust Inference Smoothing Techniques https://purl.org/becyt/ford/1.1 https://purl.org/becyt/ford/1 |
| Sumario: | The problem of testing the null hypothesis that the regression functions of two populations are equal versus one-sided alternatives under a general nonparametric homoscedastic regression model is considered. To protect against atypical observations, the test statistic is based on the residuals obtained by using a robust estimate for the regression function under the null hypothesis. The asymptotic distribution of the test statistic is studied under the null hypothesis and under root−n local alternatives. A Monte Carlo study is performed to compare the finite sample behaviour of the proposed tests with the classical one obtained using local averages. A sensitivity analysis is carried on a real data set. |
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