Change Point Tests Based on U-Statistics with Applications in Reliability.
A change-point problem is examined from a Bayesian viewpoint, under nonparametric hypotheses. A Ferguson-Dirichlet prior is chosen and the posterior distribution is computed for the change-point and for the unknown distribution functions.
In this paper we present a simulation study to analyze the behavior of the -divergence test statistics in the problem of goodness-of-fit for loglinear models with linear constraints and multinomial sampling. We pay special attention to the Rényi’s and -divergence measures.
Data depth is an important concept of nonparametric approach to multivariate data analysis. The main aim of the paper is to review possible applications of the data depth, including outlier detection, robust and affine-equivariant estimates of location, rank tests for multivariate scale difference, control charts for multivariate processes, and depth-based classifiers solving discrimination problem.