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最新文章

• MM algorithms for generalized Bradley-Terry models AI翻译
  作者：Hunter, DR
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：The Bradley-Terry model for paired comparisons is a simple and much-studied means to describe the probabilities of the possible outcomes when individuals are judged against one another in pairs. Among the many studies of the model in the past 75 years, numerous authors have generalized it in several directions, sometimes providing iterative algorithms for obtaining maximum likelihood estimates for the generalizations. Building on a theory of algorithms known by the initials MM, for minorizatio...
• Multinomial-Poisson homogeneous models for contingency tables AI翻译
  作者：Lang, JB
  作者单位：University of Iowa
  摘要：A unified approach to maximum likelihood inference for a broad, new class of contingency table models is presented. The model class comprises multinomial-Poisson homogeneous (MPH) models, which can be characterized by an independent sampling plan and a system of homogeneous constraints, h(m) - 0, where m is the vector of expected table counts. Maximum likelihood (ML) fitting and large-sample inference for MPH models are described. The MPH models are partitioned into well-defined equivalence cl...
• Optimal aggregation of classifiers in statistical learning AI翻译
  作者：Tsybakov, AB
  作者单位：Universite Paris Cite; Sorbonne Universite
  摘要：Classification can be considered as nonparametric estimation of sets, where the risk is defined by means of a specific distance between sets associated with misclassification error. It is shown that the rates of convergence of classifiers depend on two parameters: the complexity of the class of candidate sets and the margin parameter. The dependence is explicitly given, indicating that optimal fast rates approaching O (n(-1)) can be attained, where n is the sample size, and that the proposed c...
• Statistical behavior and consistency of classification methods based on convex risk minimization AI翻译
  作者：Zhang, T
  作者单位：International Business Machines (IBM); IBM USA
  摘要：We study how closely the optimal Bayes error rate can be approximately reached using a classification algorithm that computes a classifier by minimizing a convex upper bound of the classification error function. The measurement of closeness is characterized by the loss function used in the estimation. We show that such a classification scheme can be generally regarded as a (nonmaximum-likelihood) conditional in-class probability estimate, and we use this analysis to compare various convex loss...
• The golden chain AI翻译
  作者：Bickel, PJ; Ritov, Y
  作者单位：University of California System; University of California Berkeley; Hebrew University of Jerusalem
• Consistent estimation of distributions with type II bias with applications in competing risks problems AI翻译
  作者：El Barmi, H; Mukerjee, H
  作者单位：City University of New York (CUNY) System; Baruch College (CUNY); Wichita State University
  摘要：A random variable X is symmetric about 0 if X and -X have the same distribution. There is a large literature on the estimation of a distribution function (DF) under the symmetry restriction and tests for checking this symmetry assumption. Often the alternative describes some notion of skewness or one-sided bias. Various notions can be described by an ordering of the distributions of X and -X. One such important ordering is that P(0 < X less than or equal to x) - P(-x less than or equal to X < ...
• The golden chain - Rejoinder AI翻译
  作者：Lugosi, G; Vayatis, N
  作者单位：Pompeu Fabra University; Sorbonne Universite; Universite Paris Cite
• Process consistency for AdaBoost AI翻译
  作者：Jiang, WX
  作者单位：Northwestern University
  摘要：Recent experiments and theoretical studies show that AdaBoost can overfit in the limit of large time. If running the algorithm forever is suboptimal, a natural question is how low can the prediction error be during the process of AdaBoost? We show under general regularity conditions that during the process of AdaBoost a consistent prediction is generated, which has the prediction error approximating the optimal Bayes error as the sample size increases. This result suggests that, while running ...
• Asymptotic properties of doubly adaptive biased coin designs for multitreatment clinical trials AI翻译
  作者：Hu, FF; Zhang, LX
  作者单位：University of Virginia; Zhejiang University
  摘要：A general doubly adaptive biased coin design is proposed for the allocation of subjects to K treatments in a clinical trial. This design follows the same spirit as Efron's biased coin design and applies to the cases where the desired allocation proportions are unknown, but estimated sequentially. Strong consistency, a law of the iterated logarithm and asymptotic normality of this design are obtained under some widely satisfied conditions. For two treatments, a new family of designs is proposed...
• The golden chain - Rejoinder AI翻译
  作者：Zhang, T
  作者单位：International Business Machines (IBM); IBM USA
  摘要：The discussants contributed different views on several aspects of large margin classification methods and outlined some interesting future directions. I would like to thank them for the stimulating comments. In the following I will mainly focus on two issues. One is the conditional probability modeling aspect of large margin classification methods and the other is related to properties of greedy algorithms used in boosting procedures.