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贝叶斯风险的信息不等式
This paper presents lower bounds, derived from the information inequality, for the Bayes risk under scaled quadratic loss. Some numerical results are also presented which give some idea concerning the precision of these bounds. An appendix contains a proof of the information inequality without conditions on the estimator. This result is a direct extension of an earlier result of Fabian and Hannan.
截断数据尺度模型中的极小极大风险值
Let $X$ be a positive random variable with Lebesgue density $f_\theta(x)$, where $\theta$ is the scale parameter, and let $Y$ be a positive random variable independent of $X$. We consider two models of truncation: the LHS model, where the data consist only of those observations of $X$ for which $X > Y$; and the RHS model, where the data consist of those observations of $X$ for which $X \leq Y$. Consider the problem of estimating $\theta^s, s \neq 0$, under a normalized…
不满足合法密度约束的密度估计量之改进
In order to improve the rate of decrease of the IMSE for nonparametric kernel density estimators with nonrandom bandwidth beyond $O(n^{-4/5})$ all current methods must relax the constraint that the density estimate be a bona fide function, that is, be nonnegative and integrate to one. In this paper we show how to achieve similar improvement without relaxing any of these constraints. The method can also be applied for orthogonal series, adaptive orthogonal series, spline,…