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平滑分位数比估计AI 解读

We propose a novel approach to estimating the mean difference between two highly skewed distributions. The method, which we call smooth quantile ratio estimation, smooths, over percentiles, the ratio of the quantiles of the two distributions. The method defines a large class of estimators, including the sample mean difference, the maximum likelihood estimator under log-normal samples and the L-estimator. We derive asymptotic properties such as consistency and asymptotic…

球面多面体管状邻域的体积与统计推断AI 解读

For statistical procedures including Scheffe-type simultaneous confidence bounds for response surfaces and likelihood ratio tests for an additional regressor with unspecified parameters in a regression model, the confidence level or size can be expressed in terms of probabilities of the form $P\lbrack U \in D(\Gamma, \theta)\rbrack$, where $\Gamma$ is a subset of $S^m$ (the unit sphere in $R^{m + 1}), U$ is uniformly distributed in $S^m$ and $D(\Gamma, \theta)$ denotes the…

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利用预测变量约束构造多元回归同时置信界AI 解读

This article presents a method for approximating the coverage probability of simultaneous confidence bounds for multiple regression functions when the vector of predictor variables is constrained to lie in a polyhedral convex set. The method is useful because it allows one to construct simultaneous confidence intervals with prescribed coverage probability for the regression function evaluated at various settings of the predictor variables, which are narrower than bounds…

曲线回归的保守置信带AI 解读

This paper gives a method for constructing conservative Scheffe-type simultaneous confidence bands for curvilinear regression functions over finite intervals. The method is based on the use of a geometric inequality giving an upper bound for the uniform measure of the set of points within a given distance from y, an arbitrary piecewise differentiable path with finite length in $S^{k-1}$, the unit sphere in $R^k$. The upper bound is obtained by "straightening" the path so…

同时置信界的平均宽度最优性AI 解读

Simultaneous confidence bounds for multilinear regression functions over subregions $X$ of Euclidean space are defined to be $\mu$-optimal in a class of bounds $C$, if they minimize average width with respect to $\mu$ over $X$, among all bounds in $C$ with equal coverage probability. We show that for certain simultaneous confidence bounds we can find a measure $\mu$ relative to which the bounds are $\mu$-optimal in $C$, where $C$ is a large class of bounds. Such results are…

回归中 Scheffé 型置信界与等宽置信界的比较AI 解读

Conditions are given for when the Scheffé-type bound has smaller average width than the constant-width bound when coverage probabilities are equal, for bounding a multilinear regression function with intercept over one of the ellipsoidal regions of Halperin and Gurian (1968). The conditions depend on the size of the region relative to the design, and they are shown to hold for the special case of an orthogonal design, when the regressors are restricted to the boundary of the…

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