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• Nonparametric regression using local kernel estimating equations for correlated failure time data AI翻译
  作者：Yu, Zhangsheng; Lin, Xihong
  作者单位：University System of Ohio; Ohio State University; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：We study nonparametric regression for correlated failure time data. Kernel estimating equations are used to estimate nonparametric covariate effects. Independent and weighted-kernel estimating equations are studied. The derivative of the nonparametric function is first estimated and the nonparametric function is then estimated by integrating the derivative estimator. We show that the nonparametric kernel estimator is consistent for any arbitrary working correlation matrix and that its asymptot...
• Predicting cumulative incidence probability by direct binomial regression AI翻译
  作者：Scheike, Thomas H.; Zhang, Mei-Jie; Gerds, Thomas A.
  作者单位：University of Copenhagen; Medical College of Wisconsin; University of Freiburg
  摘要：We suggest a new simple approach for estimation and assessment of covariate effects for the cumulative incidence curve in the competing risks model. We consider a semiparametric regression model where some effects may be time-varying and some may be constant over time. Our estimator can be implemented by standard software. Our simulation study shows that the estimator works well and has finite-sample properties comparable with the subdistribution approach. We apply the method to bone marrow tr...
• Probability estimation for large-margin classifiers AI翻译
  作者：Wang, Junhui; Shen, Xiaotong; Liu, Yufeng
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Large margin classifiers have proven to be effective in delivering high predictive accuracy, particularly those focusing on the decision boundaries and bypassing the requirement of estimating the class probability given input for discrimination. As a result, these classifiers may not directly yield an estimated class probability, which is of interest itself. To overcome this difficulty, this article proposes a novel method for estimating the class probability through sequential classifications...
• Two-stage sampling from a prediction point of view when the cluster sizes are unknown AI翻译
  作者：Bjornstad, Jan F.; Ytterstad, Elinor
  作者单位：UiT The Arctic University of Tromso
  摘要：We consider the problem of estimating the population total in two-stage cluster sampling when cluster sizes are known only for the sampled clusters, making use of a population model arising from a variance component model. The problem can be considered as one of predicting the unobserved part Z of the total, and the concept of predictive likelihood is studied. Prediction intervals and a predictor for the population total are derived for the normal case, based on predictive likelihood. For a mo...
• Bayesian and frequentist confidence intervals arising from empirical-type likelihoods AI翻译
  作者：Chang, In Hong; Mukerjee, Rahul
  作者单位：Chosun University; Indian Institute of Management (IIM System); Indian Institute of Management Calcutta
  摘要：For a general class of empirical-type likelihoods for the population mean, higher-order asymptotics are developed with a view to characterizing its members which allow, for any given prior, the existence of a confidence interval that has approximately correct posterior as well as frequentist coverage. In particular, it is seen that the usual empirical likelihood always allows such a confidence interval, while many of its variants proposed in the literature do not enjoy this property. An explic...