A novel decoder for unknown diversity channels employing space-time codes
EURASIP Journal on Applied Signal Processing - Space-time coding and its applications - part I
Multiple Composite Hypothesis Testing: A Competitive Approach
Journal of VLSI Signal Processing Systems
Minimax Regret Classifier for Imprecise Class Distributions
The Journal of Machine Learning Research
A novel decoder for unknown diversity channels employing space-time codes
EURASIP Journal on Applied Signal Processing
Reliability criteria in information theory and in statistical hypothesis testing
Foundations and Trends in Communications and Information Theory
Identification of LOS in time-varying, frequency selective radio channels
EURASIP Journal on Wireless Communications and Networking
A Neyman-Pearson approach to universal erasure and list decoding
IEEE Transactions on Information Theory
Exact characterization of the minimax loss in error exponents of universal decoders
IEEE Transactions on Information Theory
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A novel approach is presented for the long-standing problem of composite hypothesis testing. In composite hypothesis testing, unlike in simple hypothesis testing, the probability function of the observed data, given the hypothesis, is uncertain as it depends on the unknown value of some parameter. The proposed approach is to minimize the worst case ratio between the probability of error of a decision rule that is independent of the unknown parameters and the minimum probability of error attainable given the parameters. The principal solution to this minimax problem is presented and the resulting decision rule is discussed. Since the exact solution is, in general, hard to find, and a fortiori hard to implement, an approximation method that yields an asymptotically minimax decision rule is proposed. Finally, a variety of potential application areas are provided in signal processing and communications with special emphasis on universal decoding