In this paper, we derive simple sign-based point-optimal test in linearand nonlinear regression models. The test is exact, distribution free, robustagainst heteroskedasticity of unknown form, and it may be inverted to obtaincon…dence regions for the vector of unknown parameters.Because our optimal test depends on the alternative hypothesis, we pro-pose an adaptive approa...

In this paper, we derive simple point-optimal sign-based tests in the context of linear and nonlinear regression models with fixed regressors. These tests are exact, distribution-free, robust against heteroskedasticity of unknown form, and they may be inverted to obtain confidence regions for the vector of unknown parameters. Since the point-optimal sign tests depend on the alternative hypothes...

A procedure, based on sample spacings, is proposed for testing whether a univariate distribution is symmetric about some unknown value. The proposed test is a modification of a sign test suggested by Antille and Kersting (1977), but unlike Antille and Kersting’s test, our modified test is asymptotically distribution-free and is usable in practice. A simulation study indicates that the proposed ...

This article introduces the use of fuzzy P -values and confidence intervals (Geyer and Meeden 2005) with classical nonparametric tests, specifically with the sign test and the one-sample and two-sample Wilcoxon tests. The method presented can be applied to any test and provides simple exact fuzzy tests at any level in the presence of ties. It also provides the corresponding fuzzy confidence int...

This article concerns the tests for the equality of two location parameters when the data dimension is larger than the sample size. Existing spatial-sign-based procedures are not robust with respect to high dimensionality, producing tests with the type-I error rates that aremuch larger than thenominal levels.Wedevelop a correction thatmakes the sign-based tests applicable for high-dimensional d...