Coverage of credible intervals in nonparametric monotone regression
研究了非参数单调回归中贝叶斯可信区间的覆盖概率，发现其通常高于名义置信水平，并提出重新校准方法以获得正确的渐近覆盖。
For nonparametric univariate regression under a monotonicity constraint on the regression function f, we study the coverage of a Bayesian credible interval for f(x0), where x0 is an interior point. Analysis of the posterior becomes a lot more tractable by considering a “projection-posterior” distribution based on a finite random series of step functions with normal basis coefficients as a prior for f. A sample f from the resulting conjugate posterior distribution is projected on the space of monotone increasing functions to obtain a monotone function f∗ closest to f, inducing the “projection-posterior.” We use projection-posterior samples to obtain credible intervals for f(x0). We obtain the asymptotic coverage of the credible interval thus constructed and observe that it is free of nuisance parameters involving the true function. We observe a very interesting phenomenon that the coverage is typically higher than the nominal credibility level, the opposite of a phenomenon observed by Cox (Ann. Statist. 21 (1993) 903–923) in the Gaussian sequence model. We further show that a recalibration gives the right asymptotic coverage by starting from a lower credibility level that can be explicitly calculated.