DATA-DRIVEN SMOOTH TESTS AND A DIAGNOSTIC TOOL FOR LACK-OF-FIT FOR CIRCULAR DATA

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Summary

Two contributions to the statistical analysis of circular data are given. First we construct data-driven smooth goodness-of-fit tests for the circular von Mises assumption. As a second method, we propose a new graphical diagnostic tool for the detection of lack-of-fit for circular distributions. We illustrate our methods on two real datasets.

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