Precise error rates for computationally efficient testing
在尖峰Wigner模型中，研究计算高效检验的I型和II型错误率最优权衡，发现基于线性谱统计的检验在计算受限下达到最优，但不如指数时间检验。
We revisit the fundamental question of simple-versus-simple hypothesis testing with an eye toward computational complexity, as the statistically optimal likelihood ratio test is often computationally intractable in high-dimensional settings. In the classical spiked Wigner model with a general i.i.d. spike prior, we show (conditional on a conjecture) that an existing test based on linear spectral statistics achieves the best possible trade-off curve between type-I and type-II error rates among all computationally efficient tests, even though there are exponential-time tests that do better. This result is conditional on an appropriate complexity-theoretic conjecture, namely a natural strengthening of the well-established low-degree conjecture. Our result shows that the spectrum is a sufficient statistic for computationally bounded tests (but not for all tests). To our knowledge, our approach gives the first tool for reasoning about the precise asymptotic testing error achievable with efficient computation. The main ingredients required for our hardness result are a sharp bound on the norm of the low-degree likelihood ratio along with (counterintuitively) a positive result on achievability of testing. This strategy appears to be new even in the setting of unbounded computation, in which case it gives an alternate way to analyze the fundamental statistical limits of testing.