Journal of the American Statistical Association

最新文章

• Modeling Point Referenced Spatial Count Data: A Poisson Process Approach AI翻译
  作者：Morales-Navarrete, Diego; Bevilacqua, Moreno; Caamano-Carrillo, Christian; Castro, Luis M.
  作者单位：Pontificia Universidad Catolica de Chile; Universidad Adolfo Ibanez; Universita Ca Foscari Venezia; Universidad del Bio-Bio; Pontificia Universidad Catolica de Chile
  摘要：Random fields are useful mathematical tools for representing natural phenomena with complex dependence structures in space and/or time. In particular, the Gaussian random field is commonly used due to its attractive properties and mathematical tractability. However, this assumption seems to be restrictive when dealing with counting data. To deal with this situation, we propose a random field with a Poisson marginal distribution considering a sequence of independent copies of a random field wit...
• Testing General Linear Hypotheses Under a High-Dimensional Multivariate Regression Model with Spiked Noise Covariance AI翻译
  作者：Li, Haoran; Aue, Alexander; Paul, Debashis; Peng, Jie
  作者单位：Auburn University System; Auburn University; University of California System; University of California Davis
  摘要：We consider the problem of testing linear hypotheses under a multivariate regression model with a high-dimensional response and spiked noise covariance. The proposed family of tests consists of test statistics based on a weighted sum of projections of the data onto the estimated latent factor directions, with the weights acting as the regularization parameters. We establish asymptotic normality of the test statistics under the null hypothesis. We also establish the power characteristics of the...
• Online Statistical Inference for Stochastic Optimization via Kiefer-Wolfowitz Methods AI翻译
  作者：Chen, Xi; Lai, Zehua; Li, He; Zhang, Yichen
  作者单位：New York University; University of Chicago; Purdue University System; Purdue University
  摘要：This article investigates the problem of online statistical inference of model parameters in stochastic optimization problems via the Kiefer-Wolfowitz algorithm with random search directions. We first present the asymptotic distribution for the Polyak-Ruppert-averaging type Kiefer-Wolfowitz (AKW) estimators, whose asymptotic covariance matrices depend on the distribution of search directions and the function-value query complexity. The distributional result reflects the tradeoff between statis...
• Optimal and Safe Estimation for High-Dimensional Semi-Supervised Learning AI翻译
  作者：Deng, Siyi; Ning, Yang; Zhao, Jiwei; Zhang, Heping
  作者单位：Cornell University; University of Wisconsin System; University of Wisconsin Madison; Yale University
  摘要：We consider the estimation problem in high-dimensional semi-supervised learning. Our goal is to investigate when and how the unlabeled data can be exploited to improve the estimation of the regression parameters of linear model in light of the fact that such linear models may be misspecified in data analysis. We first establish the minimax lower bound for parameter estimation in the semi-supervised setting, and show that this lower bound cannot be achieved by supervised estimators using the la...
• Ultimate Polya Gamma Samplers - Efficient MCMC for Possibly Imbalanced Binary and Categorical Data AI翻译
  作者：Zens, Gregor; Fruhwirth-Schnatter, Sylvia; Wagner, Helga
  作者单位：International Institute for Applied Systems Analysis (IIASA); Johannes Kepler University Linz
  摘要：Modeling binary and categorical data is one of the most commonly encountered tasks of applied statisticians and econometricians. While Bayesian methods in this context have been available for decades now, they often require a high level of familiarity with Bayesian statistics or suffer from issues such as low sampling efficiency. To contribute to the accessibility of Bayesian models for binary and categorical data, we introduce novel latent variable representations based on Polya-Gamma random ...
• Multivariate Sparse Clustering for Extremes AI翻译
  作者：Meyer, Nicolas; Wintenberger, Olivier
  作者单位：Centre National de la Recherche Scientifique (CNRS); Universite de Montpellier; Inria; Sorbonne Universite; Universite Paris Cite; University of Vienna
  摘要：Identifying directions where extreme events occur is a significant challenge in multivariate extreme value analysis. In this article, we use the concept of sparse regular variation introduced by Meyer and Wintenberger to infer the tail dependence of a random vector X. This approach relies on the Euclidean projection onto the simplex which better exhibits the sparsity structure of the tail of X than the standard methods. Our procedure based on a rigorous methodology aims at capturing clusters o...
• Instrumental Variable Estimation of Marginal Structural Mean Models for Time-Varying Treatment AI翻译
  作者：Michael, Haben; Cui, Yifan; Lorch, Scott A.; Tchetgen, Eric Tchetgen J.
  作者单位：University of Massachusetts System; University of Massachusetts Amherst; Zhejiang University; University of Pennsylvania; University of Pennsylvania
  摘要：Robins introduced Marginal Structural Models (MSMs), a general class of counterfactual models for the joint effects of time-varying treatment regimes in complex longitudinal studies subject to time-varying confounding. In his work, identification of MSM parameters is established under a Sequential Randomization Assumption (SRA), which rules out unmeasured confounding of treatment assignment over time. We consider sufficient conditions for identification of the parameters of a subclass, Margina...
• Intraday Periodic Volatility Curves AI翻译
  作者：Andersen, Torben G.; Su, Tao; Todorov, Viktor; Zhang, Zhiyuan
  作者单位：National Bureau of Economic Research; Northwestern University; Zhejiang Gongshang University; Northwestern University; Shanghai University of Finance & Economics; Northwestern University
  摘要：The volatility of financial asset returns displays pronounced variation over the trading day. Our goal is nonparametric inference for the average intraday volatility pattern, viewed as a function of time-of-day. The functional inference is based on a long span of high-frequency return data. Our setup allows for general forms of volatility dynamics, including time-variation in the intraday pattern. The estimation is based on forming local volatility estimates from the high-frequency returns ove...
• Nonlinear Causal Discovery with Confounders AI翻译
  作者：Li, Chunlin; 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：This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlated Gaussian errors due to confounding. First, we derive model identifiability under the sublinear growth assumption. Then, we propose a novel method, named the Deconfounded Functional Structure Estimation (DeFuSE), consisting of a deconfounding adjustment to remove the confounding effects and a sequential procedure to estimate the causal order of variables. We implement D...
• Projection Test for Mean Vector in High Dimensions AI翻译
  作者：Liu, Wanjun; Yu, Xiufan; Zhong, Wei; Li, Runze
  作者单位：University of Notre Dame; Xiamen University; Xiamen University; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park
  摘要：This article studies the projection test for high-dimensional mean vectors via optimal projection. The idea of projection test is to project high-dimensional data onto a space of low dimension such that traditional methods can be applied. We first propose a new estimation for the optimal projection direction by solving a constrained and regularized quadratic programming. Then two tests are constructed using the estimated optimal projection direction. The first one is based on a data-splitting ...