Checking the adequacy of a partial linear model (original) (raw)
2003
A partial linear model is a model where the response variable depends on some covariates linearly and on others nonparametrically. In this article, we construct an empirical process-based test for examining the adequacy of partial linearity of model. A re-sampling approach, called random symmetrization (RS), is applied to obtain the approximation to the null distribution of the test. The procedure is easy to implement. A simulation study is carried out and application to an example is made.
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A Permutation Approach to Goodness-of-fit Testing in Linear Regression Models
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Model checking plays an important role in linear regression as model misspecification seriously affects the validity and efficiency of regression analysis. In practice, model checking is often performed by subjectively evaluating the plot of the model’s residuals. This approach is objectified by constructing a random process from the model’s residuals, however due to a very complex covariance function obtaining the exact distribution of the test statistic is intractable. Several solutions to overcome this have been proposed, however the simulation and bootstrap based approaches are only asymptotically valid and can, with a limited sample size, yield tests which have inappropriate size. We therefore propose to estimate the null distribution by using permutations. We show, under some mild assumptions, that with homoscedastic random errors this yields consistent tests under the null and the alternative hypotheses. Small sample properties of the proposed tests are studied in an extensiv...
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