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L1回归中的杠杆与崩溃AI 解读

In this article the notion of leverage of a design point when fitting a linear regression model is interpreted geometrically. In the case of least squares fitting, the leverage indicators based on the diagonal of the hat matrix are widely applied. By interpreting these hat matrix indicators geometrically, leverage can be generalized to groups of design points, as well as to other methods of fitting. The article introduces a leverage indicator that is appropriate for L 1…

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位置参数的稳健置信区间：构型方法AI 解读

A conditional approach to robustness is described and applied to interval estimation for a location parameter. Two new types of confidence interval estimators using two-dimensional numerical integrations within location-and-scale configurations emerge. Strong confidence intervals have robust conditional coverage probabilities. Bioptimal confidence intervals are as short as possible and have robust overall coverage probabilities. Robust intervals compromising the Gaussian and…

位置参数的稳健置信区间：构型方法AI 解读

A conditional approach to robustness is described and applied to interval estimation for a location parameter. Two new types of confidence interval estimators using two-dimensional numerical integrations within location-and-scale configurations emerge. Strong confidence intervals have robust conditional coverage probabilities. Bioptimal confidence intervals are as short as possible and have robust overall coverage probabilities. Robust intervals compromising the Gaussian and…

构型位置估计量的渐近性质AI 解读

This paper examines the asymptotic properties of compromise estimators. By this we mean an estimation method which compromises between a finite number of sampling situations in a small sample optimal way. We develop the asymptotic theory of such estimators in the location problem and show that under a specific choice of a pair of sampling situations the compromise estimator is asymptotically robust in Huber's sense.