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构造具有最优期望大小的置信区域AI 解读

This article presents a Monte Carlo method for approximating the minimax expected size (MES) confidence set for a parameter known to lie in a compact set. The algorithm is motivated by problems in the physical sciences in which parameters are unknown physical constants related to the distribution of observable phenomena through complex numerical models. The method repeatedly draws parameters at random from the parameter space and simulates data as if each of those values…

收缩估计量、Skorokhod 问题与随机分部积分AI 解读

For a broad class of error distributions that includes the spherically symmetric ones, we give a short proof that the usual estimator of the mean in a d-dimensional shift model is inadmissible under quadratic loss when $d \geq 3$. Our proof involves representing the error distribution as that of a stopped Brownian motion and using elementary stochastic analysis to obtain a generalization of an integration by parts lemma due to Stein in the Gaussian case.

更不易包含零的非等变同时置信区间AI 解读

We present a procedure for finding simultaneous confidence intervals for the expectations μ = (μ j ) n j=1 of a set of independent random variables, identically distributed up to their location parameters, that yields intervals less likely to contain zero than the standard simultaneous confidence intervals for many μ ≠ 0. The procedure is defined implicitly by inverting a nonequivariant hypothesis test with a hyperrectangular acceptance region whose orientation depends on…

更不易包含零的非等变同时置信区间AI 解读

We present a procedure for finding simultaneous confidence intervals for the expectations μ = (μ j ) n j=1 of a set of independent random variables, identically distributed up to their location parameters, that yields intervals less likely to contain zero than the standard simultaneous confidence intervals for many μ ≠ 0. The procedure is defined implicitly by inverting a nonequivariant hypothesis test with a hyperrectangular acceptance region whose orientation depends on…

形状约束密度的有限样本置信包络AI 解读

A conservative finite-sample simultaneous confidence envelope for a density can be found by solving a finite set of finite-dimensional linear programming problems if the density is known to be monotonic or to have at most $k$ modes relative to a positive weight function. The dimension of the problems is at most $(n/\log n)^{1/3}$, where $n$ is the number of observations. The linear programs find densities attaining the largest and smallest values at a point among cumulative…