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- W2594836963 abstract "We present a scheme for determining the number of signals common to or correlated across multiple data sets. Handling multiple data sets is challenging due to the different possible correlation structures. For two data sets, the signals are either correlated or uncorrelated between the data sets. For multiple data sets, however, there are numerous combinations how the signals can be correlated. Prior studies dealing with multiple data sets all assume a particular correlation structure. In this paper, we present a technique based on a series of hypothesis tests and the bootstrap, which works for arbitrary correlation structure. Numerical results show that the proposed technique correctly detects the number of correlated signals in scenarios where the competition tends to overestimate." @default.
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- W2594836963 date "2016-11-01" @default.
- W2594836963 modified "2023-10-18" @default.
- W2594836963 title "Bootstrap-based detection of the number of signals correlated across multiple data sets" @default.
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- W2594836963 doi "https://doi.org/10.1109/acssc.2016.7869115" @default.
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