2004

最新文章

• Sieve empirical likelihood ratio tests for nonparametric functions AI翻译
  作者：Fan, JQ; Zhang, J
  作者单位：Princeton University; Chinese University of Hong Kong; University of Kent; Chinese Academy of Sciences
  摘要：Generalized likelihood ratio statistics have been proposed in Fan, Zhang and Zhang [Ann. Statist. 29 (2001) 153-193] as a generally applicable method for testing norparametic hypotheses about nonparametric functions. The likelihood ratio statistics are constructed based on the assumption that the distributions of stochastic errors are in a certain parametric family. We extend their work to the case where the error distribution is completely unspecified via newly proposed sieve empirical likeli...
• Statistical estimation in the proportional hazards model with risk set sampling AI翻译
  作者：Chen, K
  作者单位：Hong Kong University of Science & Technology
  摘要：Thomas' partial likelihood estimator of regression parameters is widely used in the analysis of nested case-control data with Cox's model. This paper proposes a new estimator of the regression parameters, which is consistent and asymptotically normal. Its asymptotic variance is smaller than that of Thomas' estimator away from the null. Unlike some other existing estimators, the proposed estimator does not rely on any more data than strictly necessary for Thomas' estimator and is easily computa...
• Least angle regression - Discussion AI翻译
  作者：Knight, K
  作者单位：University of Toronto
• Least angle regression - Discussion AI翻译
  作者：Weisberg, S
  作者单位：University of Minnesota System; University of Minnesota Twin Cities
• On the testability of the car assumption AI翻译
  作者：Cator, EA
  作者单位：Delft University of Technology
  摘要：In recent years a popular nonparametric model for coarsened data is an assumption on the coarsening mechanism called coarsening at random (CAR). It has been conjectured in several papers that this assumption cannot be tested by the data, that is, the assumption does not restrict the possible distributions of the data. In this paper we will show that this conjecture is not always true; an example will be current status data. We will also give conditions when the conjecture is true, and in doing...
• Least angle regression - Discussion AI翻译
  作者：Madigan, D; Ridgeway, G
  作者单位：Avaya; Rutgers University System; Rutgers University New Brunswick; Rutgers University System; Rutgers University New Brunswick; RAND Corporation; Rand Health
• 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.