3期

最新文章

• Direct estimation of low-dimensional components in additive models AI翻译
  作者：Fan, JQ; Härdle, W; Mammen, E
  作者单位：University of North Carolina; University of North Carolina Chapel Hill; Humboldt University of Berlin; Ruprecht Karls University Heidelberg
  摘要：Additive regression models have turned out to be a useful statistical tool in analyses of high-dimensional data sets. Recently, an estimator of additive components has been introduced by Linton and Nielsen which is based on marginal integration. The explicit definition of this estimator makes possible a fast computation and allows an asymptotic distribution theory. In this paper an asymptotic treatment of this estimate is offered for several models. A modification of this procedure is introduc...
• The strong law under random truncation AI翻译
  作者：He, SY; Yang, GL
  作者单位：Peking University; University System of Maryland; University of Maryland College Park
  摘要：The random truncation model is defined by the conditional probability distribution H(x, y) = P[X less than or equal to x, Y less than or equal to y\X greater than or equal to Y] where X and Y are independent random variables. A problem of interest is the estimation of the distribution function F of X with data from the distribution H. Under random truncation, F need not be fully identifiable from H and only a part of it, say F-0, is. We show that the nonparametric MLE F-n of F-0 obeys the stro...
• Arcing classifiers - Discussion AI翻译
  作者：Freund, Y; Schapire, RE
  作者单位：AT&T
• Deconvolution density estimation on SO(N) AI翻译
  作者：Kim, PT
  作者单位：University of Guelph; Yonsei University
  摘要：This paper develops nonparametric deconvolution density estimation over SO(N), the group of N x N orthogonal matrices of determinant 1. The methodology is to use the group and manifold structures to adapt the Euclidean deconvolution techniques to this Lie group environment. This is achieved by employing the theory of group representations explicit to SO(N). General consistency results are obtained with specific rates of convergence achieved under sufficient smoothness conditions. Application t...
• Arcing classifiers AI翻译
  作者：Breiman, L
  作者单位：University of California System; University of California Berkeley
  摘要：Recent work has shown that combining multiple versions of unstable classifiers such as trees or neural nets results in reduced test set error. One of the more effective is bagging. Here, modified training sets are formed by resampling from the original training set, classifiers constructed using these training sets and then combined by voting. Freund and Schapire propose an algorithm the basis of which is to adaptively resample and combine (hence the acronym arcing) so that the weights in the ...
• Minimax estimation via wavelet shrinkage AI翻译
  作者：Donoho, DL; Johnstone, IM
  作者单位：Stanford University
  摘要：We attempt to recover an unknown function from noisy, sampled data. Using orthonormal bases of compactly supported wavelets, we develop a nonlinear method which works in the wavelet domain by simple nonlinear shrinkage of the empirical wavelet coefficient. The shrinkage can be tuned to be nearly minimax over any member of a wide range of Triebel- and Besov-type smoothness constraints and asymptotically minimax over Besov bodies with p less than or equal to q. Linear estimates cannot achieve ev...
• Local linear regression for generalized linear models with missing data AI翻译
  作者：Wang, CY; Wang, SJ; Gutierrez, RG; Carroll, RJ
  作者单位：Fred Hutchinson Cancer Center; Southern Methodist University; Texas A&M University System; Texas A&M University College Station; Humboldt University of Berlin
  摘要：Fan, Heckman and Wand proposed locally weighted kernel polynomial regression methods for generalized linear models and quasilikelihood functions. When the covariate variables are missing at random, we propose a weighted estimator based on the inverse selection probability weights. Distribution theory is derived when the selection probabilities are estimated nonparametrically. We show that the asymptotic variance of the resulting nonparametric estimator of the mean function in the main regressi...
• Optimum robust testing in linear models AI翻译
  作者：Müller, C
  作者单位：University of Gottingen
  摘要：Robust tests for Linear models are derived via Wald-type tests that are based on asymptotically linear estimators. For a robustness criterion, the maximum asymptotic bias of the level of the test for distributions in a shrinking contamination neighborhood is used. By also regarding the asymptotic power of the test, admissible robust tests and most-efficient robust tests are derived. For the greatest efficiency, the determinant of the covariance matrix of the underlying estimator is minimized. ...
• On the average run length to false alarm in surveillance problems which possess an invariance structure AI翻译
  作者：Yakir, B
  作者单位：Hebrew University of Jerusalem
  摘要：Surveillance can be based, in some change-point detection problems, on a sequence of invariant statistics. Gordon and Pollak prove that, under certain conditions, the average run length (ARL) to false alarm of invariance-based Shiryayev-Roberts detection schemes is asymptotically the same as that of the dual classical scheme that is based on the original sequence of observations. In this paper we give alternative conditions under which the two ARLs coincide and demonstrate that these condition...
• Breakdown properties of location M-estimators AI翻译
  作者：Zhang, J; Li, GY
  作者单位：Chinese Academy of Sciences
  摘要：In this article, we consider the asymptotic behavior of three kinds of sample breakdown points. It is shown that for the location M-estimator with bounded objective function, both the addition sample breakdown point and the simplified replacement sample breakdown point strongly converge to the gross-error asymptotic breakdown point, whereas the replacement sample breakdown point strongly converges to a smaller value. In addition, it is proved that under some regularity conditions these sample ...