Journal of the American Statistical Association

最新文章

• Doubly Robust Interval Estimation for Optimal Policy Evaluation in Online Learning AI翻译
  作者：Shen, Ye; Cai, Hengrui; Song, Rui
  作者单位：North Carolina State University; University of California System; University of California Irvine
  摘要：Evaluating the performance of an ongoing policy plays a vital role in many areas such as medicine and economics, to provide crucial instructions on the early-stop of the online experiment and timely feedback from the environment. Policy evaluation in online learning thus attracts increasing attention by inferring the mean outcome of the optimal policy (i.e., the value) in real-time. Yet, such a problem is particularly challenging due to the dependent data generated in the online environment, t...
• A Kernel Measure of Dissimilarity between M Distributions AI翻译
  作者：Huang, Zhen; Sen, Bodhisattva
  作者单位：Columbia University
  摘要：Given M >= 2 distributions defined on a general measurable space, we introduce a nonparametric (kernel) measure of multi-sample dissimilarity (KMD)-a parameter that quantifies the difference between the M distributions. The population KMD, which takes values between 0 and 1, is 0 if and only if all the M distributions are the same, and 1 if and only if all the distributions are mutually singular. Moreover, KMD possesses many properties commonly associated with f-divergences such as the data pr...
• Quantitative Methods for Precision Medicine: Pharmacogenomics in Action AI翻译
  作者：Berg, Arthur; Wu, Rongling
  作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Penn State Health
• A Projection Space-Filling Criterion and Related Optimality Results AI翻译
  作者：Shi, Chenlu; Xu, Hongquan
  作者单位：Colorado State University System; Colorado State University Fort Collins; University of California System; University of California Los Angeles
  摘要：Computer experiments call for space-filling designs. Recently, a minimum aberration type space-filling criterion was proposed to rank and assess a family of space-filling designs including Latin hypercubes and strong orthogonal arrays. It aims at capturing the space-filling properties of a design when projected onto subregions of various sizes. In this article, we also consider the dimension aside from the sizes of subregions by proposing first an expanded space-filling hierarchy principle and...
• Testing Directed Acyclic Graph via Structural, Supervised and Generative Adversarial Learning AI翻译
  作者：Shi, Chengchun; Zhou, Yunzhe; Li, Lexin
  作者单位：University of London; London School Economics & Political Science; University of California System; University of California Berkeley
  摘要：In this article, we propose a new hypothesis testing method for directed acyclic graph (DAG). While there is a rich class of DAG estimation methods, there is a relative paucity of DAG inference solutions. Moreover, the existing methods often impose some specific model structures such as linear models or additive models, and assume independent data observations. Our proposed test instead allows the associations among the random variables to be nonlinear and the data to be time-dependent. We bui...
• Censored Interquantile Regression Model with Time-Dependent Covariates AI翻译
  作者：Chu, Chi Wing; Sit, Tony
  作者单位：City University of Hong Kong; Chinese University of Hong Kong
  摘要：Conventionally, censored quantile regression stipulates a specific, pointwise conditional quantile of the survival time given covariates. Despite its model flexibility and straightforward interpretation, the pointwise formulation oftentimes yields rather unstable estimates across neighboring quantile levels with large variances. In view of this phenomenon, we propose a new class of quantile-based regression models with time-dependent covariates for censored data. The models proposed aim to cap...
• Cohesion and Repulsion in Bayesian Distance Clustering AI翻译
  作者：Natarajan, Abhinav; De Iorio, Maria; Heinecke, Andreas; Mayer, Emanuel; Glenn, Simon
  作者单位：University of Oxford; National University of Singapore; University of London; University College London; Agency for Science Technology & Research (A*STAR); Yale NUS College; University of Oxford; University of Oxford
  摘要：Clustering in high-dimensions poses many statistical challenges. While traditional distance-based clustering methods are computationally feasible, they lack probabilistic interpretation and rely on heuristics for estimation of the number of clusters. On the other hand, probabilistic model-based clustering techniques often fail to scale and devising algorithms that are able to effectively explore the posterior space is an open problem. Based on recent developments in Bayesian distance-based clu...
• Bayesian Modeling with Spatial Curvature Processes AI翻译
  作者：Halder, Aritra; Banerjee, Sudipto; Dey, Dipak K.
  作者单位：Drexel University; University of California System; University of California Los Angeles; University of Connecticut
  摘要：Spatial process models are widely used for modeling point-referenced variables arising from diverse scientific domains. Analyzing the resulting random surface provides deeper insights into the nature of latent dependence within the studied response. We develop Bayesian modeling and inference for rapid changes on the response surface to assess directional curvature along a given trajectory. Such trajectories or curves of rapid change, often referred to as wombling boundaries, occur in geographi...
• Anomaly Detection for a Large Number of Streams: A Permutation-Based Higher Criticism Approach AI翻译
  作者：Stoepker, Ivo, V; Castro, Rui M.; Arias-Castro, Ery; van den Heuvel, Edwin
  作者单位：Eindhoven University of Technology; University of California System; University of California San Diego; University of California System; University of California San Diego
  摘要：Anomaly detection when observing a large number of data streams is essential in a variety of applications, ranging from epidemiological studies to monitoring of complex systems. High-dimensional scenarios are usually tackled with scan-statistics and related methods, requiring stringent modeling assumptions for proper calibration. In this work we take a nonparametric stance, and propose a permutation-based variant of the higher criticism statistic not requiring knowledge of the null distributio...
• Subspace Estimation with Automatic Dimension and Variable Selection in Sufficient Dimension Reduction AI翻译
  作者：Zeng, Jing; Mai, Qing; Zhang, Xin
  作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; State University System of Florida; Florida State University
  摘要：Sufficient dimension reduction (SDR) methods target finding lower-dimensional representations of a multivariate predictor to preserve all the information about the conditional distribution of the response given the predictor. The reduction is commonly achieved by projecting the predictor onto a low-dimensional subspace. The smallest such subspace is known as the Central Subspace (CS) and is the key parameter of interest for most SDR methods. In this article, we propose a unified and flexible f...