545期

最新文章

• An Algebraic Estimator for Large Spectral Density Matrices AI翻译
  作者：Barigozzi, Matteo; Farne, Matteo
  作者单位：University of Bologna; University of Bologna
  摘要：We propose a new estimator of high-dimensional spectral density matrices, called ALgebraic Spectral Estimator (ALSE), under the assumption of an underlying low rank plus sparse structure, as typically assumed in dynamic factor models. The ALSE is computed by minimizing a quadratic loss under a nuclear norm plus ti norm constraint to control the latent rank and the residual sparsity pattern. The loss function requires as input the classical smoothed periodogram estimator and two threshold param...
• To Adjust or not to Adjust? Estimating the Average Treatment Effect in Randomized Experiments with Missing Covariates AI翻译
  作者：Zhao, Anqi; Ding, Peng
  作者单位：National University of Singapore; University of California System; University of California Berkeley
  摘要：Randomized experiments allow for consistent estimation of the average treatment effect based on the difference in mean outcomes without strong modeling assumptions. Appropriate use of pretreatment covariates can further improve the estimation efficiency. Missingness in covariates is nevertheless common in practice, and raises an important question: should we adjust for covariates subject to missingness, and if so, how? The unadjusted difference in means is always unbiased. The complete-covaria...
• Fair Policy Targeting AI翻译
  作者：Viviano, Davide; Bradic, Jelena
  作者单位：Stanford University; Harvard University; University of California System; University of California San Diego; University of California System; University of California San Diego
  摘要：One of the major concerns of targeting interventions on individuals in social welfare programs is discrimination: individualized treatments may induce disparities across sensitive attributes such as age, gender, or race. This article addresses the question of the design of fair and efficient treatment allocation rules. We adopt the nonmaleficence perspective of first do no harm : we select the fairest allocation within the Pareto frontier. We cast the optimization into a mixed-integer linear p...
• A Mass-Shifting Phenomenon of Truncated Multivariate Normal Priors AI翻译
  作者：Zhou, Shuang; Ray, Pallavi; Pati, Debdeep; Bhattacharya, Anirban
  作者单位：Arizona State University; Arizona State University-Tempe; Eli Lilly; Lilly Research Laboratories; Texas A&M University System; Texas A&M University College Station
  摘要：We show that lower-dimensional marginal densities of dependent zero-mean normal distributions truncated to the positive orthant exhibit a mass-shifting phenomenon. Despite the truncated multivariate normal density having a mode at the origin, the marginal density assigns increasingly small mass near the origin as the dimension increases. The phenomenon accentuates with stronger correlation between the random variables. This surprising behavior has serious implications toward Bayesian constrain...
• A Semiparametric Inverse Reinforcement Learning Approach to Characterize Decision Making for Mental Disorders AI翻译
  作者：Guo, Xingche; Zeng, Donglin; Wang, Yuanjia
  作者单位：Columbia University; University of Michigan System; University of Michigan; Columbia University; Columbia University
  摘要：Major depressive disorder (MDD) is one of the leading causes of disability-adjusted life years. Emerging evidence indicates the presence of reward processing abnormalities in MDD. An important scientific question is whether the abnormalities are due to reduced sensitivity to received rewards or reduced learning ability. Motivated by the probabilistic reward task (PRT) experiment in the EMBARC study, we propose a semiparametric inverse reinforcement learning (RL) approach to characterize the re...
• Selective Inference for Hierarchical Clustering AI翻译
  作者：Gao, Lucy L.; Bien, Jacob; Witten, Daniela
  作者单位：University of British Columbia; University of Southern California; University of Washington; University of Washington Seattle; University of Washington; University of Washington Seattle
  摘要：Classical tests for a difference in means control the Type I error rate when the groups are defined a priori. However, when the groups are instead defined via clustering, then applying a classical test yields an extremely inflated Type I error rate. Notably, this problem persists even if two separate and independent datasets are used to define the groups and to test for a difference in their means. To address this problem, in this article, we propose a selective inference approach to test for ...
• 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...
• 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...