Testing model assumptions in functional regression models
| Autor: | Holger Dette, Axel Bücher, Gabriele Wieczorek |
|---|---|
| Rok vydání: | 2011 |
| Předmět: |
Statistics and Probability
Goodness-of-fit tests Numerical Analysis Stochastic process Tests for heteroscedasticity Sample (statistics) Functional data Regression Nominal level Goodness of fit Statistics Applied mathematics Functional regression Statistics Probability and Uncertainty Parametric bootstrap Variance function Mathematics Statistical hypothesis testing |
| Zdroj: | Journal of Multivariate Analysis. 102:1472-1488 |
| ISSN: | 0047-259X |
| DOI: | 10.1016/j.jmva.2011.05.014 |
| Popis: | In the functional regression model where the responses are curves, new tests for the functional form of the regression and the variance function are proposed, which are based on a stochastic process estimating L2-distances. Our approach avoids the explicit estimation of the functional regression and it is shown that normalized versions of the proposed test statistics converge weakly. The finite sample properties of the tests are illustrated by means of a small simulation study. It is also demonstrated that for small samples, bootstrap versions of the tests improve the quality of the approximation of the nominal level. |
| Databáze: | OpenAIRE |
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