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加权重复中位数平滑与滤波AI 解读

We propose weighted repeated median filters and smoothers for robust nonparametric regression in general and for robust online signal extraction from time series in particular. The new methods allow us to remove outlying sequences and to preserve discontinuities (shifts) in the underlying regression function (the signal) in the presence of local linear trends. Suitable weighting of the observations according to their distances in the design space reduces the bias arising…

《崩溃与群》讨论的补遗AI 解读

In his discussion of Davies and Gather [Ann. Statist. 33 (2005) 977–1035] Tyler pointed out that the theory developed there could not be applied to the case of directional data. He related the breakdown of directional functionals to the problem of definability. In this addendum we provide a concept of breakdown defined in terms of definability and not in terms of bias. If a group of finite order k acts on the sample space we show that the breakdown point can be bounded above…

多元异常值识别规则的掩蔽崩溃点AI 解读

In this article, we consider simultaneous outlier identification rules for multivariate data, generalizing the concept of so-called α outlier identifiers, as presented by Davies and Gather for the case of univariate samples. Such multivariate outlier identifiers are based on estimators of location and covariance. Therefore, it seems reasonable that characteristics of the estimators influence the behavior of outlier identifiers. Several authors mentioned that using estimators…

关于 Stahel–Donoho 估计量中 Tyler 的 MAD 修正AI 解读

In 1994 Tyler proposed a modification of the MAD in order to achieve the maximal possible finite sample breakdown point for the Stahel-Donoho estimator based upon the median and the modified MAD. A proof is not given, however, and as far as we know has not been published yet. Moreover there seems to be an error concerning the exact definition of the modified MAD which is needed to achieve this goal. For the sake of theoretical completeness and in order to correct this error…

多个异常值的识别AI 解读

One approach to identifying outliers is to assume that the outliers have a different distribution from the remaining observations. In this article we define outliers in terms of their position relative to the model for the good observations. The outlier identification problem is then the problem of identifying those observations that lie in a so-called outlier region. Methods based on robust statistics and outward testing are shown to have the highest possible breakdown…