The general formulas for the Neyman-Pearson type-II error exponent subject to two different type-I error constraints, as indicated in the title of the correspondence, are established. As revealed in the formulas, the type-II error exponents are fully determined by the ultimate statistical characteristic of the normalized log-likelihood ratio evaluated under the null hypothesis distribution. Applications of the general formulas to distributed Neyman-Pearson detection, and the channel reliability function are also demonstrated.
- Channel reliability function
- Distributed detection
- Error exponent
- Neyman-Pearson hypothesis testing