Journal of the American Statistical Association

最新文章

• Estimating the Spectral Density at Frequencies Near Zero AI翻译
  作者：McElroy, Tucker; Politis, Dimitris N.
  作者单位：University of California System; University of California San Diego; University of California System; University of California San Diego
  摘要：Estimating the spectral density function f(w) for some w is an element of[-pi,pi] has been traditionally performed by kernel smoothing the periodogram and related techniques. Kernel smoothing is tantamount to local averaging, that is, approximating f(w) by a constant over a window of small width. Although f(w) is uniformly continuous and periodic with period 2 pi, in this article we recognize the fact that w = 0 effectively acts as a boundary point in the underlying kernel smoothing problem, a...
• Hidden Markov Polya Trees for High-Dimensional Distributions AI翻译
  作者：Awaya, Naoki; Ma, Li
  作者单位：Duke University
  摘要：The Polya tree (PT) process is a general-purpose Bayesian nonparametric model that has found wide application in a range of inference problems. It has a simple analytic form and the posterior computation boils down to beta-binomial conjugate updates along a partition tree over the sample space. Recent development in PT models shows that performance of these models can be substantially improved by (i) allowing the partition tree to adapt to the structure of the underlying distributions and (ii)...
• Heavy-Tailed Density Estimation AI翻译
  作者：Tokdar, Surya T.; Jiang, Sheng; Cunningham, Erika L.
  作者单位：Duke University; University of California System; University of California Santa Cruz
  摘要：A novel statistical method is proposed and investigated for estimating a heavy tailed density under mild smoothness assumptions. Statistical analyses of heavy-tailed distributions are susceptible to the problem of sparse information in the tail of the distribution getting washed away by unrelated features of a hefty bulk. The proposed Bayesian method avoids this problem by incorporating smoothness and tail regularization through a carefully specified semiparametric prior distribution, and is a...
• Bayesian Landmark-Based Shape Analysis of Tumor Pathology Images AI翻译
  作者：Zhang, Cong; Bedi, Tejasv; Moon, Chul; Xie, Yang; Chen, Min; Li, Qiwei
  作者单位：University of Texas System; University of Texas Dallas; Southern Methodist University; University of Texas System; University of Texas Southwestern Medical Center
  摘要：Medical imaging is a form of technology that has revolutionized the medical field over the past decades. Digital pathology imaging, which captures histological details at the cellular level, is rapidly becoming a routine clinical procedure for cancer diagnosis support and treatment planning. Recent developments in deep-learning methods have facilitated tumor region segmentation from pathology images. The traditional shape descriptors that characterize tumor boundary roughness at the anatomical...
• Factor Augmented Sparse Throughput Deep ReLU Neural Networks for High Dimensional Regression AI翻译
  作者：Fan, Jianqing; Gu, Yihong
  作者单位：Princeton University
  摘要：This article introduces a Factor Augmented Sparse Throughput (FAST) model that uses both latent factors and sparse idiosyncratic components for nonparametric regression. It contains many popular statistical models. The FAST model bridges factor models on one end and sparse nonparametric models on the other end. It encompasses structured nonparametric models such as factor augmented additive models and sparse low-dimensional nonparametric interaction models and covers the cases where the covari...
• A Feasibility Study of Differentially Private Summary Statistics and Regression Analyses with Evaluations on Administrative and Survey Data AI翻译
  作者：Barrientos, Andres F.; Williams, Aaron R.; Snoke, Joshua; Bowen, Claire McKay
  作者单位：State University System of Florida; Florida State University; Urban Institute; RAND Corporation
  摘要：Federal administrative data, such as tax data, are invaluable for research, but because of privacy concerns, access to these data is typically limited to select agencies and a few individuals. An alternative to sharing microlevel data is to allow individuals to query statistics without directly accessing the confidential data. This article studies the feasibility of using differentially private (DP) methods to make certain queries while preserving privacy. We also include new methodological ad...
• Hierarchical Neyman-Pearson Classification for Prioritizing Severe Disease Categories in COVID-19 Patient Data AI翻译
  作者：Wang, Lijia; Wang, Y. X. Rachel; Li, Jingyi Jessica; Tong, Xin
  作者单位：City University of Hong Kong; University of Sydney; University of California System; University of California Los Angeles; University of Southern California
  摘要：COVID-19 has a spectrum of disease severity, ranging from asymptomatic to requiring hospitalization. Understanding the mechanisms driving disease severity is crucial for developing effective treatments and reducing mortality rates. One way to gain such understanding is using a multi-class classification framework, in which patients' biological features are used to predict patients' severity classes. In this severity classification problem, it is beneficial to prioritize the identification of m...
• Random Fixed Boundary Flows AI翻译
  作者：Yao, Zhigang; Xia, Yuqing; Fan, Zengyan
  作者单位：National University of Singapore; Zhejiang University of Finance & Economics; Singapore University of Social Sciences (SUSS); National University of Singapore
  摘要：We consider fixed boundary flows with canonical interpretability as principal components extended on nonlinear Riemannian manifolds. We aim to find a flow with fixed start and end points for noisy multivariate datasets lying near an embedded nonlinear Riemannian manifold. In geometric terms, the fixed boundary flow is defined as an optimal curve that moves in the data cloud with two fixed end points. At any point on the flow, we maximize the inner product of the vector field, which is calculat...
• A Unified Inference for Predictive Quantile Regression AI翻译
  作者：Liu, Xiaohui; Long, Wei; Peng, Liang; Yang, Bingduo
  作者单位：Jiangxi University of Finance & Economics; Jiangxi University of Finance & Economics; Tulane University; University System of Georgia; Georgia State University; Guangdong University of Finance & Economics
  摘要：The asymptotic behavior of quantile regression inference becomes dramatically different when it involves a persistent predictor with zero or nonzero intercept. Distinguishing various properties of a predictor is empirically challenging. In this article, we develop a unified predictability test for quantile regression regardless of the presence of intercept and persistence of a predictor. The developed test is a novel combination of data splitting, weighted inference, and a random weighted boot...
• Confidently Comparing Estimates with the c-value AI翻译
  作者：Trippe, Brian L.; Deshpande, Sameer K.; Broderick, Tamara
  作者单位：Columbia University; University of Wisconsin System; University of Wisconsin Madison; Massachusetts Institute of Technology (MIT)
  摘要：Modern statistics provides an ever-expanding toolkit for estimating unknown parameters. Consequently, applied statisticians frequently face a difficult decision: retain a parameter estimate from a familiar method or replace it with an estimate from a newer or more complex one. While it is traditional to compare estimates using risk, such comparisons are rarely conclusive in realistic settings. In response, we propose the c-value as a measure of confidence that a new estimate achieves smaller l...