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聚类数据的秩和检验AI 解读

The Wilcoxon rank-sum test is widely used to test the equality of two populations, because it makes fewer distributional assumptions than parametric procedures such as the t-test. However, the Wilcoxon rank-sum test can be used only if data are independent. When data are clustered, tests based on generalized estimating equations (GEEs) that generalize the t-test have been proposed. Here we develop a rank-sum test that can be used when data are clustered. As an application,…

区间删失比例风险模型中基于插补失效时间的推断AI 解读

We propose an approach to the proportional hazards model for interval-censored data in which parameter estimates are obtained by solving estimating equations that are the partial likelihood score equations for the full-data proportional hazards model, averaged over all rankings of imputed failure times consistent with the observed censoring intervals. Imputed failure times are generated using a parametric estimate of the baseline distribution; the parameters of the baseline…

正值创新一阶自回归模型的 Bootstrap 推断AI 解读

In this article we consider statistical inference for the autoregressive parameter of a first-order autoregressive sequence with positive innovations via an extreme value estimator ϕ. We show that a bootstrap procedure correctly estimates the sampling distribution of an asymptotically pivotal quantity (whose distribution depends only on the exponent of regular variation of the innovation distribution) based on ϕ, provided that the ratio of the bootstrap sample size m and the…

正创新一阶自回归模型的自助法推断AI 解读

In this article we consider statistical inference for the autoregressive parameter of a first-order autoregressive sequence with positive innovations via an extreme value estimator ϕ. We show that a bootstrap procedure correctly estimates the sampling distribution of an asymptotically pivotal quantity (whose distribution depends only on the exponent of regular variation of the innovation distribution) based on ϕ, provided that the ratio of the bootstrap sample size m and the…

核密度估计 L1 误差的若干非渐近界AI 解读

In this paper we obtain uniform upper bounds for the $L_1$ error of kernel estimators in estimating monotone densities and densities of bounded variation. The bounds are nonasymptotic and optimal in $n$, the sample size. For the bounded variation class, it is also optimal wrt an upper bound of the total variation. The proofs employ a one-sided kernel technique and are extremely simple.

紧参数空间指数族中贝叶斯复合估计量的渐近最优性AI 解读

The problem of finding admissible, asymptotically optimal compound rules is pursued in the infinite state case. The components involve the estimation of an arbitrary continuous transform of the natural parameter of a real exponential family with compact parameter space. We show that all Bayes estimators are admissible. Our main result is that any Bayes compound estimator versus a mixture of i.i.d. priors on the compound parameter is asymptotically optimal if the mixing…

后验混合分布的一致性及其应用AI 解读

Consider i.i.d. pairs $(\theta_i, X_i), i \geq 1$, where $\theta_1$ has an unknown prior distribution $\omega$ and given $\theta_1, X_1$ has distribution $P_{\theta_1}$. This setup arises naturally in the empirical Bayes problems. We put a probability (a hyperprior) on the space of all possible $\omega$ and consider the posterior mean $\hat{\omega}$ of $\omega$. We show that, under reasonable conditions, $P_{\hat{\omega}} = \int P_\theta d\hat{\omega}$ is consistent in $L_1$.…