Abstract Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarsening at random (CAR) to force them to be single-valued. Using the PASS data as an example, we re-illustrate the impossibility to test CAR and contrast it to another type of uninformative coarsening called subgroup independence (SI). It turns out that SI is testable
Whereas kernel measures of independence have been widely applied in machine learning (notably in ker...
Statistical hypothesis testing is one of the most powerful and interpretable tools for arriving at r...
New statistics are proposed for testing the hypothesis that arbitrary random variables are mutually ...
Abstract Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarse...
Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarsening at r...
Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarsening at r...
The notion of coarsening at random CAR was introduced by Heitjan and Rubin to describe the most gen...
In recent years a popular nonparametric model for coarsened data is an assumption on the coarsening ...
Missing and censored data are common types of coarse data. An important consequence of the stochasti...
We show that the class of conditional distributions satisfying the coarsening at random (CAR) proper...
Paper is devoted to investigating classical normalized empirical process of independence. Processes ...
We study the problem of nonparametric dependence detection. Many existing methods may suffer severe ...
We are now witnessing a rapid growth of a new part of group theory which has become known as "...
When dealing with incomplete data in statistical learning, or incomplete observations in probabilist...
When dealing with incomplete data in statistical learning, or incomplete observations in probabilist...
Whereas kernel measures of independence have been widely applied in machine learning (notably in ker...
Statistical hypothesis testing is one of the most powerful and interpretable tools for arriving at r...
New statistics are proposed for testing the hypothesis that arbitrary random variables are mutually ...
Abstract Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarse...
Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarsening at r...
Since coarse(ned) data naturally induce set-valued estimators, analysts often assume coarsening at r...
The notion of coarsening at random CAR was introduced by Heitjan and Rubin to describe the most gen...
In recent years a popular nonparametric model for coarsened data is an assumption on the coarsening ...
Missing and censored data are common types of coarse data. An important consequence of the stochasti...
We show that the class of conditional distributions satisfying the coarsening at random (CAR) proper...
Paper is devoted to investigating classical normalized empirical process of independence. Processes ...
We study the problem of nonparametric dependence detection. Many existing methods may suffer severe ...
We are now witnessing a rapid growth of a new part of group theory which has become known as "...
When dealing with incomplete data in statistical learning, or incomplete observations in probabilist...
When dealing with incomplete data in statistical learning, or incomplete observations in probabilist...
Whereas kernel measures of independence have been widely applied in machine learning (notably in ker...
Statistical hypothesis testing is one of the most powerful and interpretable tools for arriving at r...
New statistics are proposed for testing the hypothesis that arbitrary random variables are mutually ...