Using Residuals Robustly I: Tests for Heteroscedasticity, Nonlinearity

Revisiting Tests for Neglected Nonlinearity Using Artificial Neural Networks

Tests for regression neglected nonlinearity based on artificial neural networks (ANNs) have so far been studied by separately analyzing the two ways in which the null of regression linearity can hold. This implies that the asymptotic behavior of general ANN-based tests for neglected nonlinearity is still an open question. Here we analyze a convenient ANN-based quasi-likelihood ratio statistic f...

Improved Surrogate Data for Nonlinearity Tests.

Current tests for nonlinearity compare a time series to the null hypothesis of a Gaussian linear stochastic process. For this restricted null assumption, random surrogates can be constructed which are constrained by the linear properties of the data. We propose a more general null hypothesis allowing for nonlinear rescalings of a Gaussian linear process. We show that such rescalings cannot be a...

Robustly Anchoring Annotations Using Keywords Robustly Anchoring Annotations Using Keywords

As the number of systems which offer annotations on digital documents increases, robust anchoring of annotations is a key challenge. It is especially important if the annotator lacks write-access to the document being annotated. For example, if someone makes an annotation on a web page today and the web page changes tomorrow, what happens to the annotation? One approach is simply to “orphan” th...

Tests of linear hypotheses in the ANOVA under heteroscedasticity

It is often of interest to undertake a general linear hypothesis testing (GLHT) problem in the one-way ANOVA without assuming the equality of the group variances. When the equality of the group variances is valid, it is well known that the GLHT problem can be solved by the classical F-test. The classical F test, however, may lead to misleading conclusions when the variance homogeneity assumptio...

The Effects of Heteroscedasticity on Tests of Equivalence