3 papers
cs.IT2022
Universal Neyman-Pearson Classification with a Known Hypothesis
Parham Boroumand, Albert Guillén i Fàbregas
We propose a universal classifier for binary Neyman-Pearson classification where null distribution is known while only a training sequence is available for the alternative distribu…
cs.IT2021
Mismatched Binary Hypothesis Testing: Error Exponent Sensitivity
Parham Boroumand, Albert Guillén i Fàbregas
We study the problem of mismatched binary hypothesis testing between i.i.d. distributions. We analyze the tradeoff between the pairwise error probability exponents when the actual…
cs.IT2020
Error Exponents of Mismatched Likelihood Ratio Testing
Parham Boroumand, Albert Guillen i Fabregas
We study the problem of mismatched likelihood ratio test. We analyze the type-\RNum{1} and \RNum{2} error exponents when the actual distributions generating the observation are dif…