2期

最新文章

• Likelihood approaches for the invariant density ratio model with biased-sampling data 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：The full likelihood approach in statistical analysis is regarded as the most efficient means for estimation and inference. For complex length-biased failure time data, computational algorithms and theoretical properties are not readily available, especially when a likelihood function involves infinite-dimensional parameters. Relying on the invariance property of length-biased failure time data under the semiparametric density ratio model, we present two likelihood approaches for the estimation...
• Analysing bivariate survival data with interval sampling and application to cancer epidemiology AI翻译
  作者：Zhu, Hong; Wang, Mei-Cheng
  作者单位：University System of Ohio; Ohio State University; Johns Hopkins University; Johns Hopkins Bloomberg School of Public Health
  摘要：In biomedical studies, ordered bivariate survival data are frequently encountered when bivariate failure events are used as outcomes to identify the progression of a disease. In cancer studies, interest could be focused on bivariate failure times, for example, time from birth to cancer onset and time from cancer onset to death. This paper considers a sampling scheme, termed interval sampling, in which the first failure event is identified within a calendar time interval, the time of the initia...
• Efficient estimation for the Cox model with varying coefficients AI翻译
  作者：Chen, Kani; Lin, Huazhen; Zhou, Yong
  作者单位：Hong Kong University of Science & Technology; Southwestern University of Finance & Economics - China; Chinese Academy of Sciences
  摘要：A proportional hazards model with varying coefficients allows one to examine the extent to which covariates interact nonlinearly with an exposure variable. A global partial likelihood method, in contrast with the local partial likelihood method of Fan et al. (2006), is proposed for estimation of varying coefficient functions. The proposed estimators are proved to be consistent and asymptotically normal. Semiparametric efficiency of the estimators is demonstrated in terms of their linear functi...
• Multiple imputation in quantile regression AI翻译
  作者：Wei, Ying; Ma, Yanyuan; Carroll, Raymond J.
  作者单位：Columbia University; Texas A&M University System; Texas A&M University College Station
  摘要：We propose a multiple imputation estimator for parameter estimation in a quantile regression model when some covariates are missing at random. The estimation procedure fully utilizes the entire dataset to achieve increased efficiency, and the resulting coefficient estimators are root-n consistent and asymptotically normal. To protect against possible model misspecification, we further propose a shrinkage estimator, which automatically adjusts for possible bias. The finite sample performance of...
• Structuring shrinkage: some correlated priors for regression AI翻译
  作者：Griffin, J. E.; Brown, P. J.
  作者单位：University of Kent
  摘要：This paper develops a rich class of sparsity priors for regression effects that encourage shrinkage of both regression effects and contrasts between effects to zero whilst leaving sizeable real effects largely unshrunk. The construction of these priors uses some properties of normal-gamma distributions to include design features in the prior specification, but has general relevance to any continuous sparsity prior. Specific prior distributions are developed for serial dependence between regres...
• Stochastic blockmodels with a growing number of classes AI翻译
  作者：Choi, D. S.; Wolfe, P. J.; Airoldi, E. M.
  作者单位：Harvard University; University of London; University College London; Harvard University
  摘要：We present asymptotic and finite-sample results on the use of stochastic blockmodels for the analysis of network data. We show that the fraction of misclassified network nodes converges in probability to zero under maximum likelihood fitting when the number of classes is allowed to grow as the root of the network size and the average network degree grows at least poly-logarithmically in this size. We also establish finite-sample confidence bounds on maximum-likelihood blockmodel parameter esti...
• A generalized Dunnett test for multi-arm multi-stage clinical studies with treatment selection AI翻译
  作者：Magirr, D.; Jaki, T.; Whitehead, J.
  作者单位：Lancaster University
  摘要：We generalize the Dunnett test to derive efficacy and futility boundaries for a flexible multi-arm multi-stage clinical trial for a normally distributed endpoint with known variance. We show that the boundaries control the familywise error rate in the strong sense. The method is applicable for any number of treatment arms, number of stages and number of patients per treatment per stage. It can be used for a wide variety of boundary types or rules derived from alpha-spending functions. Addition...
• Global optimality of nonconvex penalized estimators AI翻译
  作者：Kim, Yongdai; Kwon, Sunghoon
  作者单位：Seoul National University (SNU); University of Minnesota System; University of Minnesota Twin Cities
  摘要：Nonconvex penalties such as the smoothly clipped absolute deviation or minimax concave penalties have desirable properties such as the oracle property, even when the dimension of the predictive variables is large. However, checking whether a given local minimizer has such properties is not easy since there can be many local minimizers. In this paper, we give sufficient conditions under which a local minimizer is unique, and show that the oracle estimator becomes the unique local minimizer with...
• Improved double-robust estimation in missing data and causal inference models AI翻译
  作者：Rotnitzky, Andrea; Lei, Quanhong; Sued, Mariela; Robins, James M.
  作者单位：Universidad Torcuato Di Tella; University of Buenos Aires; Harvard University; Harvard T.H. Chan School of Public Health
  摘要：Recently proposed double-robust estimators for a population mean from incomplete data and for a finite number of counterfactual means can have much higher efficiency than the usual double-robust estimators under misspecification of the outcome model. In this paper, we derive a new class of double-robust estimators for the parameters of regression models with incomplete cross-sectional or longitudinal data, and of marginal structural mean models for cross-sectional data with similar efficiency ...
• Componentwise classification and clustering of functional data AI翻译
  作者：Delaigle, A.; Hall, P.; Bathia, N.
  作者单位：University of Melbourne
  摘要：The infinite dimension of functional data can challenge conventional methods for classification and clustering. A variety of techniques have been introduced to address this problem, particularly in the case of prediction, but the structural models that they involve can be too inaccurate, or too abstract, or too difficult to interpret, for practitioners. In this paper, we develop approaches to adaptively choose components, enabling classification and clustering to be reduced to finite-dimension...