Ashwin Ram, Aaditya Ramdas
This paper establishes a tight law of the iterated logarithm for empirical KL-infinity statistics, applicable to very general data conditions.
In the world of statistics and algorithms, understanding how certain measures behave is crucial for improving performance. One such measure, called the empirical KL-infinity, is important for designing efficient algorithms and tests. This paper addresses limitations in previous studies by providing a new, more comprehensive understanding of how this measure behaves, even when dealing with data that isn't restricted to certain bounds. This advancement helps in creating more reliable algorithms for various applications.
