Journal of the American Statistical Association

最新文章

• Permutation Tests for Infection Graphs AI翻译
  作者：Khim, Justin; Loh, Po-Ling
  作者单位：Carnegie Mellon University; University of Wisconsin System; University of Wisconsin Madison; Columbia University
  摘要：We formulate and analyze a novel hypothesis testing problem for inferring the edge structure of an infection graph. In our model, a disease spreads over a network via contagion or random infection, where the times between successive contagion events are independent exponential random variables with unknown rate parameters. A subset of nodes is also censored uniformly at random. Given the observed infection statuses of nodes in the network, the goal is to determine the underlying graph. We pres...
• A Bayesian Hierarchical CACE Model Accounting for Incomplete Noncompliance With Application to a Meta-analysis of Epidural Analgesia on Cesarean Section AI翻译
  作者：Zhou, Jincheng; Hodges, James S.; Chu, Haitao
  作者单位：Amgen; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Noncompliance with assigned treatments is a common challenge in analyzing and interpreting randomized clinical trials (RCTs). One way to handle noncompliance is to estimate the complier-average causal effect (CACE), the intervention's efficacy in the subpopulation that complies with assigned treatment. In a two-step meta-analysis, one could first estimate CACE for each study, then combine them to estimate the population-averaged CACE. However, when some trials do not report noncompliance data,...
• Optimal Individualized Decision Rules Using Instrumental Variable Methods (vol 116, pg 174, 2021) AI翻译
  作者：Qiu, Hongxiang; Carone, Marco; Sadikova, Ekaterina; Petukhova, Maria; Kessler, Ronald C.; Luedtke, Alex
• Scalable Collaborative Ranking for Personalized Prediction AI翻译
  作者：Dai, Ben; Shen, Xiaotong; Wang, Junhui; Qu, Annie
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; City University of Hong Kong; University of Illinois System; University of Illinois Urbana-Champaign
  摘要：Personalized prediction presents an important yet challenging task, which predicts user-specific preferences on a large number of items given limited information. It is often modeled as certain recommender systems focusing on ordinal or continuous ratings, as in collaborative filtering and content-based filtering. In this article, we propose a new collaborative ranking system to predict most-preferred items for each user given search queries. Particularly, we propose a psi-ranker based on rank...
• Comments on A Gibbs Sampler for a Class of Random Convex Polytopes AI翻译
  作者：Hoffman, Kentaro; Hannig, Jan; Zhang, Kai
  作者单位：University of North Carolina; University of North Carolina Chapel Hill
• Bayesian Semiparametric Longitudinal Drift-Diffusion Mixed Models for Tone Learning in Adults AI翻译
  作者：Paulon, Giorgio; Llanos, Fernando; Chandrasekaran, Bharath; Sarkar, Abhra
  作者单位：University of Texas System; University of Texas Austin; University of Texas System; University of Texas Austin; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh
  摘要：Understanding how adult humans learn nonnative speech categories such as tone information has shed novel insights into the mechanisms underlying experience-dependent brain plasticity. Scientists have traditionally examined these questions using longitudinal learning experiments under a multi-category decision making paradigm. Drift-diffusion processes are popular in such contexts for their ability to mimic underlying neural mechanisms. Motivated by these problems, we develop a novel Bayesian s...
• Comment: Individualized Treatment Rules Under Endogeneity AI翻译
  作者：Han, Sukjin
  作者单位：University of Bristol
• Incorporating Animal Movement Into Distance Sampling AI翻译
  作者：Glennie, R.; Buckland, S. T.; Langrock, R.; Gerrodette, T.; Ballance, L. T.; Chivers, S. J.; Scott, M. D.
  作者单位：University of St Andrews; University of Bielefeld; National Oceanic Atmospheric Admin (NOAA) - USA
  摘要：Distance sampling is a popular statistical method to estimate the density of wild animal populations. Conventional distance sampling represents animals as fixed points in space that are detected with an unknown probability that depends on the distance between the observer and the animal. Animal movement can cause substantial bias in density estimation. Methods to correct for responsive animal movement exist, but none account for nonresponsive movement independent of the observer. Here, an expl...
• Training Neural Networks as Learning Data-Adaptive Kernels: Provable Representation and Approximation Benefits AI翻译
  作者：Dou, Xialiang; Liang, Tengyuan
  作者单位：University of Chicago; University of Chicago
  摘要：Consider the problem: given the data pair drawn from a population with , specify a neural network model and run gradient flow on the weights over time until reaching any stationarity. How does f(t), the function computed by the neural network at time t, relate to , in terms of approximation and representation? What are the provable benefits of the adaptive representation by neural networks compared to the prespecified fixed basis representation in the classical nonparametric literature? We ans...
• Covariance-Based Sample Selection for Heterogeneous Data: Applications to Gene Expression and Autism Risk Gene Detection AI翻译
  作者：Lin, Kevin Z.; Liu, Han; Roeder, Kathryn
  作者单位：Carnegie Mellon University; Northwestern University
  摘要：Risk for autism can be influenced by genetic mutations in hundreds of genes. Based on findings showing that genes with highly correlated gene expressions are functionally interrelated, guilt by association methods such as DAWN have been developed to identify these autism risk genes. Previous research analyzes the BrainSpan dataset, which contains gene expression of brain tissues from varying regions and developmental periods. Since the spatiotemporal properties of brain tissue are known to aff...