487期

最新文章

• Attributing Effects to a Cluster-Randomized Get-Out-the-Vote Campaign AI翻译
  作者：Hansen, Ben B.; Bowers, Jake
  作者单位：University of Michigan System; University of Michigan; University of Illinois System; University of Illinois Urbana-Champaign; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Early in the twentieth century, Fisher and Neyman demonstrated how to infer effects of agricultural interventions using only the very weakest of assumptions, by randomly varying which plots were to be manipulated. Although the methods permitted uncontrolled variation between experimental units. the), required strict control over assignment of interventions; this hindered their application to field studies With human subjects, who ordinarily could not be compelled to comply with experimenters' ...
• Jackknife Empirical Likelihood AI翻译
  作者：Jing, Bing-Yi; Yuan, Junqing; Zhou, Wang
  作者单位：Hong Kong University of Science & Technology; National University of Singapore
  摘要：Empirical likelihood has been found very useful in many different occasions. However, when applied directly to some more complicated statistics such as U-statistics, it runs into serious computational difficulties. In this paper, we introduce a so-called jackknife empirical likelihood (JEL) method. The new method is extremely simple to use in practice. In particular. the JEL is shown to be very effective in handling one and two-sample U-statistics. The JEL can be potentially useful for other n...
• Nonparametric Prediction in Measurement Error Models Comment AI翻译
  作者：Fan, Jianqing; Feng, Yang
  作者单位：Princeton University
• Nonparametric Prediction in Measurement Error Models Comment AI翻译
  作者：Schennach, Susanne M.
  作者单位：University of Chicago
• Analyzing Length-Biased Data With Semiparametric Transformation and Accelerated Failure Time Models AI翻译
  作者：Shen, Yu; Ning, Jing; Qin, Jing
  作者单位：University of Texas System; UTMD Anderson Cancer Center; National Institutes of Health (NIH) - USA; NIH National Institute of Allergy & Infectious Diseases (NIAID)
  摘要：Right-censored time-to-event data are often observed from a cohort of prevalent cases that are subject to length-biased sampling. Informative right censoring of data from the prevalent cohort within the population often makes it difficult to model risk factors on the unbiased failure times for the general population. because the observed failure times are length biased. In this paper. we consider two classes of flexible semiparametric models: the transformation models and the accelerated failu...
• Assessing Robustness of Intrinsic Tests of Independence in Two-Way Contingency Tables AI翻译
  作者：Casella, George; Moreno, Elias
  作者单位：State University System of Florida; University of Florida; University of Granada
  摘要：For testing nested hypotheses from a Bayesian standpoint, a desirable condition is that the prior for the alternative model concentrates mass around the smaller, or null, model. For testing independence in contingency tables, the intrinsic priors satisfy this requirement. Furthermore. the degree of concentration of the priors is controlled by a discrete parameter, t, the training sample size, which plays an important role in the resulting answer. In this article we report on the robustness of ...
• A Semiparametric Regression Cure Model for Interval-Censored Data AI翻译
  作者：Liu, Hao; Shen, Yu
  作者单位：Baylor College of Medicine; University of Texas System; UTMD Anderson Cancer Center
  摘要：Motivated by medical Studies in which patients could be Cured of disease but the disease event time may be subject to interval censoring, we present a semiparametric nonmixture cure model for the regression analysis of interval-censored time-to-event data. We develop semiparametric maximum likelihood estimation for the model using the expectation-maximization method for interval-censored data. The maximization step for the baseline function is nonparametric and numerically challenging. We deve...
• Note: Modeling Future Record Performances in Athletics AI翻译
  作者：Hilbe, Joseph M.
  作者单位：Arizona State University
• An Incomplete-Data Quasi-Likelihood Approach to Haplotype-Based Genetic Association Studies on Related Individuals AI翻译
  作者：Wang, Zuoheng; McPeek, Mary Sara
  作者单位：University of Chicago; University of Chicago
  摘要：We propose an incomplete-data, quasi-likelihood framework for estimation and score tests that accommodates both dependent and partially observed data. The motivation comes from genetic association studies, where we address the problems of estimating haplotype frequencies and testing association between a disease and haplotypes of multiple. tightly linked genetic markers, using case-control samples containing, related individuals. We consider a more general setting in which the complete data ar...