504期

最新文章

• Sampling for Conditional Inference on Network Data AI翻译
  作者：Zhang, Jingfei; Chen, Yuguo
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Random graphs with given vertex degrees have been widely used as a model for many real-world complex networks. However, both statistical inference and analytic study of such networks present great challenges. In this article, we propose a new sequential importance sampling method for sampling networks with a given degree sequence. These samples can be used to approximate closely the null distributions of a number of test statistics involved in such networks and provide an accurate estimate of ...
• Generalized Jackknife Estimators of Weighted Average Derivatives Comment AI翻译
  作者：Chen, Xiaohong
  作者单位：Yale University
• Cure Rate Quantile Regression for Censored Data With a Survival Fraction AI翻译
  作者：Wu, Yuanshan; Yin, Guosheng
  作者单位：Wuhan University; University of Hong Kong
  摘要：Censored quantile regression offers a valuable complement to the traditional Cox proportional hazards model for survival analysis. Survival times tend to be right-skewed, particularly when there exists a substantial fraction of long-term survivors who are either cured or immune to the event of interest. For survival data with a cure possibility, we propose cure rate quantile regression under the common censoring scheme that survival times and censoring times are conditionally independent given...
• Generalized Jackknife Estimators of Weighted Average Derivatives Rejoinder AI翻译
  作者：Cattaneo, Matias D.; Crump, Richard K.; Jansson, Michael
  作者单位：University of Michigan System; University of Michigan; Federal Reserve System - USA; Federal Reserve Bank - New York; University of California System; University of California Berkeley
• Classification Using Censored Functional Data AI翻译
  作者：Delaigle, Aurore; Hall, Peter
  作者单位：University of Melbourne
  摘要：We consider classification of functional data when the training curves are not observed on the same interval. Different types of classifier are suggested, one of which involves a new curve extension procedure. Our approach enables us to exploit the information contained in the endpoints of these intervals by incorporating it in an explicit but flexible way. We study asymptotic properties of our classifiers, and show that, in a variety of settings, they can even produce asymptotically perfect c...
• Statistical Learning With Time Series Dependence: An Application to Scoring Sleep in Mice Comment AI翻译
  作者：Shedden, Kerby; Zeng, Donglin; Wang, Yuanjia
  作者单位：University of Michigan System; University of Michigan; University of North Carolina; University of North Carolina Chapel Hill; Columbia University
• Hidden Markov Models With Applications in Cell Adhesion Experiments AI翻译
  作者：Hung, Ying; Wang, Yijie; Zarnitsyna, Veronika; Zhu, Cheng; Wu, C. F. Jeff
  作者单位：Rutgers University System; Rutgers University New Brunswick; University System of Georgia; Georgia Institute of Technology; University System of Georgia; Georgia Institute of Technology
  摘要：Estimation of the number of hidden states is challenging in hidden Markov models. Motivated by the analysis of a specific type of cell adhesion experiments, a new framework based on a hidden Markov model and double penalized order selection is proposed. The order selection procedure is shown to be consistent in estimating the number of states. A modified expectation-maximization algorithm is introduced to efficiently estimate parameters in the model. Simulations show that the proposed framewor...