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最新文章

• A generalized EWMA control chart and its comparison with the optimal EWMA, CUSUM and GLR schemes AI翻译
  作者：Han, D; Tsung, F
  作者单位：Shanghai Jiao Tong University; Hong Kong University of Science & Technology
  摘要：It is known that both the optimal exponentially weighted moving average (EWMA) and cumulative sum (CUSUM) control charts are based on a given reference value delta, which, for the CUSUM chart, is the magnitude of a shift in the mean to be detected quickly. In this paper a generalized EWMA control chart (GEWMA) which does not depend on delta is proposed for detecting the mean shift. We compare theoretically the GEWMA control chart with the optimal EWMA, CUSUM and the generalized likelihood rati...
• Estimation of proportional covariances in the presence of certain linear restrictions AI翻译
  作者：Jensen, ST; Madsen, J
  作者单位：University of Copenhagen; Statens Serum Institut
  摘要：Proportionality of covariance matrices of n independent p-dimensional normal distributions with the same type of linear restrictions of the inverse covariances is considered. Conditions for existence and uniqueness of the maximum likelihood estimator are obtained through the development of general results for scale-invariant natural exponential families.
• The golden chain - Discussion AI翻译
  作者：Koltchinskii, V
  作者单位：University of New Mexico
• On the Stahel-Donoho estimator and depth-weighted means of multivariate data AI翻译
  作者：Zuo, YJ; Cui, HJ; He, XM
  作者单位：Michigan State University; University of Illinois System; University of Illinois Urbana-Champaign; Beijing Normal University
  摘要：The depth of multivariate data can be used to construct weighted means as robust estimators of location. The use of projection depth leads to the Stahel-Donoho estimator as a special case. In contrast to maximal depth estimators, the depth-weighted means are shown to be asymptotically normal under appropriate conditions met by depth functions commonly used in the current literature. We also confirm through a finite-sample study that the Stahel-Donoho estimator achieves a desirable balance betw...
• The 2002 Wald Memorial Lectures - Population theory for boosting ensembles AI翻译
  作者：Breiman, L
  作者单位：University of California System; University of California Berkeley
  摘要：Tree ensembles are looked at in distribution space, that is, the limit case of infinite sample size. It is shown that the simplest kind of trees is complete in D-dimensional L-2(P) space if the number of terminal nodes T is greater than D. For such trees we show that the AdaBoost algorithm gives an ensemble converging to the Bayes risk.