Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Large deviations theory is used to analyze the exponential rate of decrease of error probabilities for a sequence of decisions based on a test statistics sequence (T/sub n/). It is assumed that (for a given statistical hypothesis) the distributions of T/sub n/ are determined by some unknown member of a class of probability distributions. The worst case, or least favorably exponential rate of error probability decrease over this class, is sought. It is shown that the Legendre-Fenchel transform of the maximized cumulant function yields a lor bound for the minimized large deviations rate function, and that in many cases this bound is tight. Application of the result is illustrated by a detailed consideration of i.i.d memoryless detection with an epsilon -contamination distribution family. >
['John S. Sadowsky']
I am writing a research paper on a idea Robust large deviations performance analysis for large sample detectors
[{"from": "human", "value": "I am writing a research paper on a idea Robust large deviations performance analysis for large sample detectors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Large deviations theory is used to analyze the exponential rate of decrease of error probabilities for a sequence of decisions based on a test statistics sequence (T/sub n/). It is assumed that (for a given statistical hypothesis) the distributions of T/sub n/ are determined by some unknown member of a class of probability distributions. The worst case, or least favorably exponential rate of error probability decrease over this class, is sought. It is shown that the Legendre-Fenchel transform of the maximized cumulant function yields a lor bound for the minimized large deviations rate function, and that in many cases this bound is tight. Application of the result is illustrated by a detailed consideration of i.i.d memoryless detection with an epsilon -contamination distribution family. >"}]