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利用非参数校准进行有限总体偏差稳健估计AI 解读

A Standard problem in sample survey inference is that of predicting the finite population total H of a function h(y) of a random variable Y. The model-based approach to this problem first defines a working model ξ for Y and then predicts H by estimating its expectation under ξ, conditional on the sample values of Y. This approach leads to biased predictions if ξ is incorrect. We explore an automatic solution to this misspecification bias that uses nonparametric regression to…

大边界区域下的核回归及其在多项式模型适合性检验中的应用AI 解读

It is well known that kernel regression estimators are subject to so-called boundary or edge effects, a phenomenon in which the bias of an estimator increases near the endpoints of the estimation interval. When the regression curve is linear or nearly linear, the requisite amount of smoothing is so great that the boundary region is effectively the entire estimation interval. Special boundary kernels are proposed here to deal with such cases. It is shown that the proposed…

消除核型非参数回归边界效应的几何方法AI 解读

We introduce a simple geometric method for removing edge effects from kernel-type nonparametric regression estimators. It involves reflecting the data set in two estimated points, thereby generating a new data set with three times the range of the original data. The usual kernel-type estimator may be applied to the new, enlarged data set, without any danger of edge effects. This technique is applicable generally to both regularly spaced and randomly spaced designs and admits…

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一类最小分位数距离估计量的渐近性质AI 解读

A family of estimators based upon M specified quantiles is defined. These procedures take as the estimate the vector that minimizes a quadratic distance measure between M sample quantiles and a parametric family of quantile functions. Under regularity conditions these estimators are consistent, asymptotically normal, and robust. For a specific quadratic form the estimator is optimal among a class of asymptotically normal estimators, and it approaches full efficiency as M…

一族最小分位数距离估计量的渐近性质AI 解读

A family of estimators based upon M specified quantiles is defined. These procedures take as the estimate the vector that minimizes a quadratic distance measure between M sample quantiles and a parametric family of quantile functions. Under regularity conditions these estimators are consistent, asymptotically normal, and robust. For a specific quadratic form the estimator is optimal among a class of asymptotically normal estimators, and it approaches full efficiency as M…

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角度观测依赖的二元模型及相关马尔可夫过程AI 解读

Some new distributions having specified marginal distributions are proposed for dependent random variables taking values on the torus. A related family of Markov processes taking values on the circle is constructed, and statistical inference for these processes is studied.

若干角度—线性联合分布及相关回归模型AI 解读

Parametric models are proposed for the joint distribution of bivariate random variables when one variable is directional and one is scalar. These distributions are developed on the basis of the maximum entropy principle and by the specification of the marginal distributions. The properties of these distributions and the statistical analysis of regression models based on these distributions are explored. One model is extended to several variables in a form that justifies the…