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通过交叉验证进行稳健线性模型选择AI 解读

This article gives a robust technique for model selection in regression models, an important aspect of any data analysis involving regression. There is a danger that outliers will have an undue influence on the model chosen and distort any subsequent analysis. We provide a robust algorithm for model selection using Shao's cross-validation methods for choice of variables as a starting point. Because Shao's techniques are based on least squares, they are sensitive to outliers.…

小样本置信区间AI 解读

In this article we present a technique for constructing one- or two-sided confidence intervals, which are second-order correct in terms of coverage, for either parametric or nonparametric problems. The construction is valid in the presence of nuisance parameters. The situation we consider is this: there are p parameters and we want a confidence interval for some function of them, possibly one of the parameters itself. The p parameters are estimated by M-estimates, which…

多元 M 估计的小样本渐近展开AI 解读

Asymptotic expansions are derived for the densities of multivariate $M$-estimates. The expansion is based on a saddlepoint technique and yields good accuracy in the tails for small sample sizes. Numerical results are given for robust estimation of location and scale and these are compared with known Monte Carlo results.