2023

最新文章

• Score-Driven Modeling of Spatio-Temporal Data AI翻译
  作者：Gasperoni, Francesca; Luati, Alessandra; Paci, Lucia; D'Innocenzo, Enzo
  作者单位：University of Cambridge; MRC Biostatistics Unit; University of Bologna; Catholic University of the Sacred Heart
  摘要：A simultaneous autoregressive score-driven model with autoregressive disturbances is developed for spatio-temporal data that may exhibit heavy tails. The model specification rests on a signal plus noise decomposition of a spatially filtered process, where the signal can be approximated by a nonlinear function of the past variables and a set of explanatory variables, while the noise follows a multivariate Student-t distribution. The key feature of the model is that the dynamics of the space-tim...
• Partition-Mallows Model and Its Inference for Rank Aggregation AI翻译
  作者：Zhu, Wanchuang; Jiang, Yingkai; Liu, Jun S.; Deng, Ke
  作者单位：Tsinghua University; Tsinghua University; Tsinghua University; Tsinghua University; Harvard University
  摘要：Learning how to aggregate ranking lists has been an active research area for many years and its advances have played a vital role in many applications ranging from bioinformatics to internet commerce. The problem of discerning reliability of rankers based only on the rank data is of great interest to many practitioners, but has received less attention from researchers. By dividing the ranked entities into two disjoint groups, that is, relevant and irrelevant/background ones, and incorporating ...
• Adversarial Machine Learning: Bayesian Perspectives AI翻译
  作者：Insua, David Rios; Naveiro, Roi; Gallego, Victor; Poulos, Jason
  作者单位：Consejo Superior de Investigaciones Cientificas (CSIC); CSIC - Instituto de Ciencias Matematicas (ICMAT); CUNEF Universidad; Harvard University; Harvard Medical School
  摘要：Adversarial Machine Learning (AML) is emerging as a major field aimed at protecting Machine Learning (ML) systems against security threats: in certain scenarios there may be adversaries that actively manipulate input data to fool learning systems. This creates a new class of security vulnerabilities that ML systems may face, and a new desirable property called adversarial robustness essential to trust operations based on ML outputs. Most work in AML is built upon a game-theoretic modeling of t...
• Causal Inference in Transcriptome-Wide Association Studies with Invalid Instruments and GWAS Summary Data AI翻译
  作者：Xue, Haoran; Shen, Xiaotong; Pan, Wei
  作者单位：University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities; University of Minnesota System; University of Minnesota Twin Cities
  摘要：Transcriptome-Wide Association Studies (TWAS) have recently emerged as a popular tool to discover (putative) causal genes by integrating an outcome GWAS dataset with another gene expression/transcriptome GWAS (called eQTL) dataset. In our motivating and target application, we'd like to identify causal genes for Low-Density Lipoprotein cholesterol (LDL), which is crucial for developing new treatments for hyperlipidemia and cardiovascular diseases. The statistical principle underlying TWAS is (t...
• Interpolating Population Distributions using Public-Use Data: An Application to Income Segregation using American Community Survey Data AI翻译
  作者：Simpson, Matthew; Holan, Scott H.; Wikle, Christopher K.; Bradley, Jonathan R.
  作者单位：SAS Institute Inc; University of Missouri System; University of Missouri Columbia; State University System of Florida; Florida State University
  摘要：The presence of income inequality is an important problem to demographers, policy makers, economists, and social scientists. A causal link has been hypothesized between income inequality and income segregation, which measures how much households with similar incomes cluster. The information theory index is used to measure income segregation, however, critics have suggested the divergence index instead. Motivated by this, we construct both indices using American Community Survey (ACS) estimates...
• Online Bootstrap Inference For Policy Evaluation In Reinforcement Learning AI翻译
  作者：Ramprasad, Pratik; Li, Yuantong; Yang, Zhuoran; Wang, Zhaoran; Sun, Will Wei; Cheng, Guang
  作者单位：Purdue University System; Purdue University; University of California System; University of California Los Angeles; Yale University; Northwestern University; Purdue University System; Purdue University
  摘要：The recent emergence of reinforcement learning (RL) has created a demand for robust statistical inference methods for the parameter estimates computed using these algorithms. Existing methods for inference in online learning are restricted to settings involving independently sampled observations, while inference methods in RL have so far been limited to the batch setting. The bootstrap is a flexible and efficient approach for statistical inference in online learning algorithms, but its efficac...
• Spatio-Temporal Cross-Covariance Functions under the Lagrangian Framework with Multiple Advections AI翻译
  作者：Salvana, Mary Lai O.; Lenzi, Amanda; Genton, Marc G.
  作者单位：King Abdullah University of Science & Technology
  摘要：When analyzing the spatio-temporal dependence in most environmental and earth sciences variables such as pollutant concentrations at different levels of the atmosphere, a special property is observed: the covariances and cross-covariances are stronger in certain directions. This property is attributed to the presence of natural forces, such as wind, which cause the transport and dispersion of these variables. This spatio-temporal dynamics prompted the use of the Lagrangian reference frame alon...
• Sharp Nonparametric Bounds for Decomposition Effects with Two Binary Mediators AI翻译
  作者：Gabriel, Erin E.; Sachs, Michael C.; Sjolander, Arvid
  作者单位：University of Copenhagen; Karolinska Institutet
  摘要：In randomized trials, once the total effect of the intervention has been estimated, it is often of interest to explore mechanistic effects through mediators along the causal pathway between the randomized treatment and the outcome. In the setting with two sequential mediators, there are a variety of decompositions of the total risk difference into mediation effects. We derive sharp and valid bounds for a number of mediation effects in the setting of two sequential mediators both with unmeasure...
• Independent Nonlinear Component Analysis AI翻译
  作者：Gunsilius, Florian; Schennach, Susanne
  作者单位：University of Michigan System; University of Michigan; Brown University
  摘要：The idea of summarizing the information contained in a large number of variables by a small number of factors or principal components has been broadly adopted in statistics. This article introduces a generalization of the widely used principal component analysis (PCA) to nonlinear settings, thus providing a new tool for dimension reduction and exploratory data analysis or representation. The distinguishing features of the method include 0) the ability to always deliver truly independent (inste...
• The Generalized Oaxaca-Blinder Estimator AI翻译
  作者：Guo, Kevin; Basse, Guillaume
  作者单位：Stanford University
  摘要：After performing a randomized experiment, researchers often use ordinary least-square (OLS) regression to adjust for baseline covariates when estimating the average treatment effect. It is widely known that the resulting confidence interval is valid even if the linear model is misspecified. In this article, we generalize that conclusion to covariate adjustment with nonlinear models. We introduce an intuitive way to use any simple nonlinear model to construct a covariate-adjusted confidence int...