Biometrika

最新文章

• Simple design-efficient calibration estimators for rejective and high-entropy sampling AI翻译
  作者：Tan, Z.
  作者单位：Rutgers University System; Rutgers University New Brunswick
  摘要：For survey calibration, consider the situation where the population totals of auxiliary variables are known or where auxiliary variables are measured for all population units. For each situation, we develop design-efficient calibration estimators under rejective or high-entropy sampling. A general approach is to extend efficient estimators for missing-data problems with independent and identically distributed data to the survey setting. We show that this approach effectively resolves two long-...
• Scaled envelopes: scale-invariant and efficient estimation in multivariate linear regression AI翻译
  作者：Cook, Dennis; Su, Zhihua
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; State University System of Florida; University of Florida
  摘要：Efficient estimation of the regression coefficients is a fundamental problem in multivariate linear regression. The envelope model proposed by Cook et al. (2010) was shown to have the potential to achieve substantial efficiency gains by accounting for linear combinations of the response vector that are essentially immaterial to coefficient estimation. This requires in part that the distribution of those linear combinations be invariant to changes in the nonstochastic predictor vector. However,...
• Composite quantile regression for the receiver operating characteristic curve AI翻译
  作者：Duan, Xiaogang; Zhou, Xiao-Hua
  作者单位：Beijing Normal University; University of Washington; University of Washington Seattle
  摘要：The covariate-specific receiver operating characteristic curve is frequently used to evaluate the classification accuracy of a diagnostic test when it is associated with certain covariates. In this paper, we propose a new procedure for estimating this curve based on a reformulation of the conventional location-scale model and the idea of composite quantile regression. Asymptotic normality of the proposed estimators is established, both for the regression parameters and for the covariate-specif...
• Controlling the bias of robust small-area estimators AI翻译
  作者：Jiongo, Dongmo; Haziza, D.; Duchesne, P.
  作者单位：Statistics Canada; Universite de Montreal
  摘要：Sinha & Rao (2009) proposed estimation procedures designed for small-area means, based on robustified maximum likelihood estimators and robust empirical best linear unbiased predictors. Their methods are of the plug-in type and may be biased. Bias-corrected estimators have been proposed by Chambers et al. (2013). Here, we investigate two new approaches: one relying on the work of Chambers (1986), and the second using the concept of conditional bias to measure the influence of units in the popu...
• Practical perfect sampling using composite bounding chains: the Dirichlet-multinomial model AI翻译
  作者：Stein, Nathan M.; Meng, Xiao-Li
  作者单位：Harvard University
  摘要：A discrete data augmentation scheme together with two different parameterizations yields two Gibbs samplers for sampling from the posterior distribution of the hyperparameters of the Dirichlet-multinomial hierarchical model under a default prior distribution. The finite-state space nature of this data augmentation permits us to construct two perfect samplers using bounding chains that take advantage of monotonicity and anti-monotonicity in the target posterior distribution, but both are imprac...
• Simple design-efficient calibration estimators for rejective and high-entropy sampling (vol 100, pg 399, 2013) AI翻译
  作者：Tan, Z.
  作者单位：Rutgers University System; Rutgers University New Brunswick
• Efficiency loss and the linearity condition in dimension reduction AI翻译
  作者：Ma, Yanyuan; Zhu, Liping
  作者单位：Texas A&M University System; Texas A&M University College Station; Shanghai University of Finance & Economics
  摘要：Linearity, sometimes jointly with constant variance, is routinely assumed in the context of sufficient dimension reduction. It is well understood that, when these conditions do not hold, blindly using them may lead to inconsistency in estimating the central subspace and the central mean subspace. Surprisingly, we discover that even if these conditions do hold, using them will bring efficiency loss. This paradoxical phenomenon is illustrated through sliced inverse regression and principal Hessi...
• Karl Pearson's Biometrika: 1901-36 AI翻译
  作者：Aldrich, John
  作者单位：University of Southampton
  摘要：Karl Pearson edited Biometrika for the first 35 years of its existence. Not only did he shape the journal, he also contributed over 200 pieces and inspired, more or less directly, most of the other contributions. The journal could not be separated from the man.
• A consistent multivariate test of association based on ranks of distances AI翻译
  作者：Heller, Ruth; Heller, Yair; Gorfine, Malka
  作者单位：Tel Aviv University; Technion Israel Institute of Technology
  摘要：We consider the problem of detecting associations between random vectors of any dimension. Few tests of independence exist that are consistent against all dependent alternatives. We propose a powerful test that is applicable in all dimensions and consistent against all alternatives. The test has a simple form, is easy to implement, and has good power.
• On the likelihood ratio test for envelope models in multivariate linear regression AI翻译
  作者：Schott, James R.
  作者单位：State University System of Florida; University of Central Florida
  摘要：We investigate the likelihood ratio test for a hypothesis regarding the dimension of the Sigma-envelope of span(beta) in a multivariate linear regression model. The asymptotic null distribution of the likelihood ratio statistic is obtained as some nuisance parameters approach infinity. A saddlepoint approximation is also given for this limiting distribution. The accuracy of this approximation and its comparison to the standard chi-squared approximation are assessed via simulation. The results ...