Journal of the American Statistical Association

最新文章

• Sparse Semiparametric Nonlinear Model With Application to Chromatographic Fingerprints Rejoinder AI翻译
  作者：Wierzbicki, Michael R.; Guo, Li-Bing; Du, Qing-Tao; Guo, Wensheng
  作者单位：University of Pennsylvania; Guangdong Pharmaceutical University
• Bayesian Aggregation of Order-Based Rank Data AI翻译
  作者：Deng, Ke; Han, Simeng; Li, Kate J.; Liu, Jun S.
  作者单位：Tsinghua University; Harvard University; Suffolk University
  摘要：Rank aggregation, that is, combining several ranking functions (called base rankers) to get aggregated, usually stronger rankings of a given set of items, is encountered in many disciplines. Most methods in the literature assume that base rankers of interest are equally reliable. It is very common in practice, however, that some rankers are more informative and reliable than others. It is desirable to distinguish high quality base rankers from low quality ones and treat them differently. Some ...
• Sparse Semiparametric Nonlinear Model With Application to Chromatographic Fingerprints Comment AI翻译
  作者：Du, Pang
  作者单位：Virginia Polytechnic Institute & State University
• Efficient R-Estimation of Principal and Common Principal Components AI翻译
  作者：Hallin, Marc; Paindaveine, Davy; Verdebout, Thomas
  作者单位：Universite Libre de Bruxelles; Princeton University; Universite Libre de Bruxelles; Universite de Lille; Inria; Universite de Lille
  摘要：We propose rank-based estimators of principal components, both in the one-sample and, under the assumption of common principal components, in the m-sample cases. Those estimators are obtained via a rank-based version of Le Cam's one-step method, combined with an estimation of cross-information quantities. Under arbitrary elliptical distributions with, in the m-sample case, possibly heterogeneous radial densities, those R-estimators remain root-n consistent and asymptotically normal, while achi...
• Accurate Directional Inference for Vector Parameters in Linear Exponential Families AI翻译
  作者：Davison, A. C.; Fraser, D. A. S.; Reid, N.; Sartori, N.
  作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; University of Toronto; University of Padua
  摘要：We consider inference on a vector-valued parameter of interest in a linear exponential family, in the presence of a finite-dimensional nuisance parameter. Based on higher-order asymptotic theory for likelihood, we propose a directional test whose p-value is computed using one-dimensional integration. The work simplifies and develops earlier research on directional tests for continuous models and on higher-order inference for discrete models, and the examples include contingency tables and logi...
• Modeling Bivariate Longitudinal Hormone Profiles by Hierarchical State Space Models AI翻译
  作者：Liu, Ziyue; Cappola, Anne R.; Crofford, Leslie J.; Guo, Wensheng
  作者单位：Indiana University System; Indiana University Indianapolis; Indiana University System; Indiana University Indianapolis; University of Pennsylvania; University of Kentucky; University of Pennsylvania
  摘要：The hypothalamic-pituitary-adrenal (HPA) axis is crucial in coping with stress and maintaining homeostasis. Hormones produced by the HPA axis exhibit both complex univariate longitudinal profiles and complex relationships among different hormones. Consequently, modeling these multivariate longitudinal hormone profiles is a challenging task. In this article, we propose a bivariate hierarchical state space model, in which each hormone profile is modeled by a hierarchical state space model, with ...
• The Estimation of Leverage Effect With High-Frequency Data AI翻译
  作者：Wang, Christina D.; Mykland, Per A.
  作者单位：Princeton University; Princeton University; University of Chicago; University of Chicago
  摘要：The leverage effect has become an extensively studied phenomenon that describes the (usually) negative relation between stock returns and their volatility. Although this characteristic of stock returns is well acknowledged, most studies of the phenomenon are based on cross-sectional calibration with parametric models. On the statistical side, most previous works are conducted over daily or longer return horizons, and few of them have carefully studied its estimation, especially with high-frequ...
• Causal Inference for fMRI Time Series Data With Systematic Errors of Measurement in a Balanced On/Off Study of Social Evaluative Threat AI翻译
  作者：Sobel, Michael E.; Lindquist, Martin A.
  作者单位：Columbia University; Johns Hopkins University
  摘要：Functional magnetic resonance imaging (fMRI) has facilitated major advances in understanding human brain function. Neuroscientists are interested in using fMRI to study the effects of external stimuli on brain activity and causal relationships among brain regions, but have not stated what is meant by causation or defined the effects they purport to estimate. Building on Rubin's causal model, we construct a framework for causal inference using blood oxygenation level dependent (BOLD) fMRI time ...
• A Model-Averaging Approach for High-Dimensional Regression AI翻译
  作者：Ando, Tomohiro; Li, Ker-Chau
  作者单位：Keio University; Academia Sinica - Taiwan; University of California System; University of California Los Angeles
  摘要：This article considers high-dimensional regression problems in which the number of predictors p exceeds the sample size n. We develop a model-averaging procedure for high-dimensional regression problems. Unlike most variable selection studies featuring the identification of true predictors, our focus here is on the prediction accuracy for the true conditional mean of y given the p predictors. Our method consists of two steps. The first step is to construct a class of regression models, each wi...
• A Likelihood-Based Framework for Association Analysis of Allele-Specific Copy Numbers AI翻译
  作者：Hu, Y. J.; Lin, D. Y.; Sun, W.; Zeng, D.
  作者单位：Emory University; University of North Carolina; University of North Carolina Chapel Hill
  摘要：Copy number variants (CNVs) and single nucleotide polymorphisms (SNPs) coexist throughout the human genome and jointly contribute to phenotypic variations. Thus, it is desirable to consider both types of variants, as characterized by allele-specific copy numbers (ASCNs), in association studies of complex human diseases. Current SNP genotyping technologies capture the CNV and SNP information simultaneously via fluorescent intensity measurements. The common practice of calling ASCNs from the int...