3期

最新文章

• A statistical version of prophet inequalities AI翻译
  作者：Assaf, D; Goldstein, L; Samuel-Cahn, E
  作者单位：Hebrew University of Jerusalem; University of Southern California
  摘要：All classical prophet inequalities for independent random variables hold also in the case where only a noise-corrupted version of those variables is observable. That is, if the pairs (X-1,Z(1)),..,(X-n,Z(n)) are independent with arbitrary, known joint distributions, and only the sequence Z(1),...,Z(n) is observable, then all prophet inequalities which would hold if the X's were directly observable still hold, even though the expected X-values (i.e., the payoffs) for both the prophet and statis...
• Asymptotic and bootstrap confidence bounds for the structural average of curves AI翻译
  作者：Wang, KM; Gasser, T
  作者单位：State University of New York (SUNY) System; SUNY Downstate Health Sciences University; University of Zurich
  摘要：For analyzing samples of curves, Kneip and Gasser proposed a structural analysis for estimating an average curve which gives the average amplitude and dynamics of the sample curves. An important step of their method is the estimation of the warping functions in order to eliminate the differences in dynamics between different curves. It is of interest to compute confidence bounds for the structural average curve. First, we derive the necessary asymptotic results to obtain confidence intervals b...
• Estimation and testing for lattice conditional independence models on Euclidean Jordan algebras AI翻译
  作者：Massam, H; Neher, E
  作者单位：York University - Canada; University of Virginia; University of Ottawa
  摘要：In this paper we generalize the major results of Andersson and Perlman on LCI models to the setting of symmetric cones and give an explicit closed form formula for the estimate of the covariance matrix in the generalized LCI models that we define. To this end, we replace the cone H-I(+)(R) sitting inside the Jordan algebra of symmetric real I x I-matrices by the symmetric cone Omega of an Euclidean Jordan algebra V. We introduce the Andersson-Perlman cone Omega(K) subset of or equal to Omega w...
• Arcing classifiers - Discussion AI翻译
  作者：Amit, Y; Geman, D
  作者单位：University of Chicago; University of Massachusetts System; University of Massachusetts Amherst
• Functional stability of one-step GM-estimators in approximately linear regression AI翻译
  作者：Simpson, DG; Yohai, VJ
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; University of Buenos Aires; Consejo Nacional de Investigaciones Cientificas y Tecnicas (CONICET)
  摘要：This paper provides a comparative sensitivity analysis of one-step Newton-Raphson estimators for linear regression. Such estimators have been proposed as a way to combine the global stability of high breakdown estimators with the local stability of generalized maximum likelihood estimators. We analyze this strategy, obtaining upper bounds for the maximum bias induced by epsilon-contamination of the model. These bounds yield breakdown points and local rates of convergence of the bias as epsilon...
• Arcing classifiers - Rejoinder AI翻译
  作者：Breiman, L
  作者单位：University of California System; University of California Berkeley
• Arcing classifiers - Discussion AI翻译
  作者：Dietterich, TG
  作者单位：Oregon State University
• Block threshold rules for curve estimation using kernel and wavelet methods AI翻译
  作者：Hall, P; Kerkyacharian, G; Picard, D
  作者单位：Australian National University; Universite de Picardie Jules Verne (UPJV); Universite Paris Cite
  摘要：Motivated by recently developed threshold rules for wavelet estimators, we suggest threshold methods for general kernel density estimators, including those of classical Rosenblatt-Parzen type. Thresholding makes kernel methods competitive in terms of their adaptivity to a wide variety of aberrations in complex signals. It is argued that term-by-term thresholding does not always produce optimal performance, since individual coefficients cannot be estimated sufficiently accurately for reliable d...
• Estimation of the truncation probability in the random truncation model 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：Under random truncation, a pair of independent random variables X and Y is observable only if X is larger than Y. The resulting model is 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]. For the truncation probability alpha = P[X greater than or equal to Y], a proper estimate is not the sample proportion but alpha(n) = integral G(n)(s)dF(n)(s) where F-n and G(n) are product limit estimates of the distributio...
• Asymptotic expansions of the k nearest neighbor risk AI翻译
  作者：Snapp, RR; Venkatesh, SS
  作者单位：University of Vermont; University of Pennsylvania
  摘要：The finite-sample risk of the k nearest neighbor classifier that uses a weighted L-p-metric as a measure of class similarity is examined. For a family of classification problems with smooth distributions in R-n, an asymptotic expansion for the risk is obtained in decreasing fractional powers of the reference sample size. An analysis of the leading expansion coefficients reveals that the optimal weighted L-p-metric, that is, the metric that minimizes the finite-sample risk, tends to a weighted ...