Journal of the American Statistical Association

最新文章

• The Dependent Wild Bootstrap AI翻译
  作者：Shao, Xiaofeng
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign
  摘要：We propose a new resampling procedure. the dependent wild bootstrap. for stationary time series As a natural extension of the traditional wild bootstrap to time series setting, the dependent wild bootstrap offers a viable alternative to the existing block-based bootstrap methods. whose properties have been extensively studied over the last two decades Unlike all of the block-based bootstrap methods. the dependent wild bootstrap can be easily extended to irregularly spaced time series with no i...
• Dimension Reduction and Semiparametric Estimation of Survival Models AI翻译
  作者：Xia, Yingcun; Zhang, Dixin; Xu, Jinfeng
  作者单位：National University of Singapore; Nanjing University
  摘要：In this paper. we propose a new dimension reduction method by introducing a nominal regression model with the hazard function as the conditional mean. vs Inch naturally retrieves information from complete data and censored data as well Moreover. without requiring the linearity condition, the new method can estimate the entire central subspace consistently and exhaustively The method also provides an alternative approach or the analysis of censored data assuming neither the link function nor th...
• Composite Likelihood Bayesian Information Criteria for Model Selection in High-Dimensional Data AI翻译
  作者：Gao, Xin; Song, Peter X. -K.
  作者单位：York University - Canada; University of Michigan System; University of Michigan
  摘要：For high-dimensional data sets with complicated dependency structures, the full likelihood approach often leads to intractable computational complexity. This imposes difficulty on model selection, given that most traditionally used information criteria require evaluation of the full likelihood. We propose a composite likelihood version of the Bayes information criterion (BIC) and establish its consistency property for the selection of the true underlying marginal model. Our proposed BIC is sho...
• Dimension Reduction in Regressions Through Cumulative Slicing Estimation AI翻译
  作者：Zhu, Li-Ping; Zhu, Li-Xing; Feng, Zheng-Hui
  作者单位：Shanghai University of Finance & Economics; Hong Kong Baptist University
  摘要：In this paper we offer a complete methodology of cumulative slicing estimation to sufficient dimension reduction. In parallel to the classical slicing estimation, we develop three methods that are termed, respectively, as cumulative mean estimation, cumulative variance estimation, and cumulative directional regression. The strong consistency for p = O(n(1/2)/log n) and the asymptotic normality for p = o(n(1/2)) are established, where p is the dimension of the predictors and n is sample size. S...
• Regularized Reconstruction of Wave Height and Slope Fields From Refracted Images of Water AI翻译
  作者：Choudhury, Kingshuk Roy; O'Sullivan, Finbarr; Samanta, Mayukh; Caulliez, Guillemette; Shrira, Victor
  作者单位：University College Cork; Aix-Marseille Universite; Keele University
  摘要：Refractive imaging of wave fields in an established experimental technique We consider the associated reconstruction problem and investigate some statistically motivated refinements. Including (a) bias correction of local slope estimates. (a) regularization of directional slopes (c) spatially weighted reconstruction using the estimated variability of local slope estimates and (d) more accurate estimates of reference light profiles from tune sequence data These refinements are based on a nonpar...
• Alternative Goodness-of-Fit Tests for Linear Models AI翻译
  作者：Christensen, Ronald; Sun, Siu Kei
  作者单位：University of New Mexico
  摘要：Fan and Huang (2001) presented a goodness-of-fit test for linear models based on Fourier transformations of the residuals of the fitted model We present two mole theoretically appealing tests in which the Fourier transforms are incorporated into a tilted model We show that when suitably normalized. the new test statistics have the same as distribution as Fan and Huang's test We propose modifications to the asymptotic normalization constants to improve the small sample sizes of our tests while ...
• High-Dimensional Variable Selection for Survival Data AI翻译
  作者：Ishwaran, Hemant; Kogalur, Udaya B.; Gorodeski, Eiran Z.; Minn, Andy J.; Lauer, Michael S.
  作者单位：Cleveland Clinic Foundation; Cleveland Clinic Foundation; University of Pennsylvania; National Institutes of Health (NIH) - USA; NIH National Heart Lung & Blood Institute (NHLBI)
  摘要：The minimal depth of a maximal subtree IN a dimensionless order statistic measuring the predictiveness of a variable in a survival tree We derive the distribution of the minimal depth and use it lot high-dimensional variable selection using random survival forests In big p and small n problems (where p is the dimension and n Is the sample size). the distribution of the minimal depth reveals a ceiling effect in which a tree simply cannot be grown deep enough to properly identify predictive vari...
• Semiparametric Mean-Covariance Regression Analysis for Longitudinal Data AI翻译
  作者：Leng, Chenlei; Zhang, Weiping; Pan, Jianxin
  作者单位：National University of Singapore; Chinese Academy of Sciences; University of Science & Technology of China, CAS; University of Manchester
  摘要：Efficient estimation of the regression coefficients in longitudinal data analysis requires a correct specification of the covariance structure Existing approaches usually focus on model no the mean with specification of certain covariance structures, which may lead to inefficient or biased estimators of parameters in the mean it misspecification occurs In this article. we propose a data-driven approach based on semiparametric regression models tot the mean and the covariance simultaneously. mo...
• A New Approach to Optimal Design for Linear Models With Correlated Observations AI翻译
  作者：Zhigljavsky, Anatoly; Dette, Holger; Pepelyshev, Andrey
  作者单位：Cardiff University; Ruhr University Bochum; University of Sheffield
  摘要：We consider the problem of designing experiments for regression in the presence of correlated observations with the location model as the main example. For a fixed correlation structure approximate optimal designs are determined explicitly, and it is demonstrated that under the model assumptions made by Bickel and Herzberg (1979) for the determination of asymptotic optimal design, the designs derived in this article converge weakly to the measures obtained by these authors. We also compare the...
• Bayesian Inference With Incomplete Multinomial Data: A Problem in Pathogen Diversity AI翻译
  作者：Ahn, Kwang Woo; Chan, Kung-Sik; Bai, Ying; Kosoy, Michael
  作者单位：Medical College of Wisconsin; University of Iowa; Centers for Disease Control & Prevention - USA
  摘要：With recent advances in genetic analysis, it has become feasible to classify a pathogen into genetically distinct variants even though they apparently cause an infected subject similar symptoms. The availability of such data opens up the interesting problem of studying the spatiotemporal variation in the diversity of variants of a pathogen. Data on pathogen variants often suffer the problems of (i) low cell counts, (ii) incomplete classification due to laboratory problems (e.g., contamination)...