We introduce generalized sign tests based on K-sign depth, shortly denoted by K-depth. These so-called K-depth tests are motivated by simplicial regression depth. Since they depend only on the signs of the residuals, these test statistics are easy to comprehend and outlier robust. We show that the K-depth test with K = 2 is equivalent to the classical sign test so that K-depth tests with K > 2 are generalizations of the classical sign test. Since the K-depth test with K = 2 is equivalent to the classical sign test, it has the same drawbacks as the classical sign test. However, the generalized sign tests with K > 2 are much more powerful. We show this by deriving their behavior at observations with few sign changes. Thereby we also ...
New sign tests for testing equality of conditional distributions of two (arbitrary) adapted processe...
The goodness-of-fit problem is addressed and two among the more efficient tests presently available ...
A general approach for developing distribution-free tests for general linear models based on simplic...
The classical sign test usually provides very bad power for certain alternatives. We present a gene...
The extension of simplicial depth to robust regression, the so-called simplicial regression depth, ...
Sign tests are among the most successful procedures in multivariate nonparametric statistics. In thi...
AbstractA general approach for developing distribution free tests for general linear models based on...
The sign test was developed to examine the median difference of paired samples. Ignorance of ties - ...
This paper provides the rst documentation of the power and speci-cation of the generalized sign test...
Multivariate sign tests attracted several statisticians in the past, and it is evident from recent n...
[[abstract]]For the sign testing problem about the normal variances, we develop the heuristic testin...
We consider testing regression coefficients in high dimensional generalized linear models. By modify...
AbstractGlobal depth, tangent depth and simplicial depths for classical and orthogonal regression ar...
We simplify simplicial depth for regression and autoregressive growth processes in two directions. ...
The Sign test is a famous nonparametric test from classical statistics used to assess the one or two...
New sign tests for testing equality of conditional distributions of two (arbitrary) adapted processe...
The goodness-of-fit problem is addressed and two among the more efficient tests presently available ...
A general approach for developing distribution-free tests for general linear models based on simplic...
The classical sign test usually provides very bad power for certain alternatives. We present a gene...
The extension of simplicial depth to robust regression, the so-called simplicial regression depth, ...
Sign tests are among the most successful procedures in multivariate nonparametric statistics. In thi...
AbstractA general approach for developing distribution free tests for general linear models based on...
The sign test was developed to examine the median difference of paired samples. Ignorance of ties - ...
This paper provides the rst documentation of the power and speci-cation of the generalized sign test...
Multivariate sign tests attracted several statisticians in the past, and it is evident from recent n...
[[abstract]]For the sign testing problem about the normal variances, we develop the heuristic testin...
We consider testing regression coefficients in high dimensional generalized linear models. By modify...
AbstractGlobal depth, tangent depth and simplicial depths for classical and orthogonal regression ar...
We simplify simplicial depth for regression and autoregressive growth processes in two directions. ...
The Sign test is a famous nonparametric test from classical statistics used to assess the one or two...
New sign tests for testing equality of conditional distributions of two (arbitrary) adapted processe...
The goodness-of-fit problem is addressed and two among the more efficient tests presently available ...
A general approach for developing distribution-free tests for general linear models based on simplic...