Testing for Structural Breaks via Ordinal Pattern Dependence

Testing for Structural Breaks via Ordinal Pattern Dependence

We propose new concepts in order to analyze and model the dependence structure between two time series. Our methods rely exclusively on the order structure of the data points. Hence, the methods are stable under monotone transformations of the time series and robust against small perturbations or measurement errors. Ordinal pattern dependence can be characterized by four parameters. We propose ...

Ordinal Pattern Dependence between hydrological time series

Testing for Structural Breaks in the Evaluation of Programs

A youth homicide reduction initiative in Boston in the mid-1990s poses particular difficulties for program evaluation because it did not have a control group and the exact implementation date is unknown. A standard methodology in program evaluation is to use time series variation to compare preand post-program outcomes. Such an approach is not valid, however, when the timing of a potential brea...

Multiple Testing under Dependence via Graphical Models

Large-scale multiple testing tasks often exhibit dependence. Leveraging the dependence between individual tests is still one challenging and important problem in statistics. With recent advances in graphical models, it is feasible to use them to capture the dependence among multiple hypotheses. We propose a multiple testing procedure which is based on a Markov-random-field-coupled mixture model...

Testing Marginal Homogeneity for Ordinal Categorical Variables

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