550期

最新文章

• Contextual Dynamic Pricing with Strategic Buyers AI翻译
  作者：Liu, Pangpang; Yang, Zhuoran; Wang, Zhaoran; Sun, Will Wei
  作者单位：Purdue University System; Purdue University; Yale University; Northwestern University
  摘要：Personalized pricing, which involves tailoring prices based on individual characteristics, is commonly used by firms to implement a consumer-specific pricing policy. In this process, buyers can also strategically manipulate their feature data to obtain a lower price, incurring certain manipulation costs. Such strategic behavior can hinder firms from maximizing their profits. In this article, we study the contextual dynamic pricing problem with strategic buyers. The seller does not observe the ...
• Tyranny-of-the-Minority Regression Adjustment in Randomized Experiments AI翻译
  作者：Lu, Xin; Liu, Hanzhong
  作者单位：Tsinghua University
  摘要：Regression adjustment is widely used in the analysis of randomized experiments to improve the estimation efficiency of the treatment effect. This article reexamines a weighted regression adjustment method termed tyranny-of-the-minority (ToM), wherein units in the minority group are given greater weights. We demonstrate that ToM regression adjustment is more robust than Lin's regression adjustment with treatment-covariate interactions, even though these two regression adjustment methods are asy...
• Unlocking Retrospective Prevalent Information in EHRs-A Revisit to the Pairwise Pseudolikelihood AI翻译
  作者：Keret, Nir; Gorfine, Malka
  作者单位：Tel Aviv University
  摘要：Electronic health records offer abundant data on various diseases and health conditions, enabling researchers to explore the relationship between disease onset age and underlying risk factors. Unlike mortality data, the event of interest is nonterminal, hence, individuals can retrospectively report their disease-onset-age upon recruitment to the study. These individuals, diagnosed with the disease before entering the study, are termed prevalent. The ascertainment imposes a left truncation cond...
• Statistical Inference for Networks of High-Dimensional Point Processes AI翻译
  作者：Wang, Xu; Kolar, Mladen; Shojaie, Ali
  作者单位：University of Washington; University of Washington Seattle; University of Chicago
  摘要：Fueled in part by recent applications in neuroscience, the multivariate Hawkes process has become a popular tool for modeling the network of interactions among high-dimensional point process data. While evaluating the uncertainty of the network estimates is critical in scientific applications, existing methodological and theoretical work has primarily addressed estimation. To bridge this gap, we develop a new statistical inference procedure for high-dimensional Hawkes processes. The key ingred...
• Testing a Large Number of Composite Null Hypotheses Using Conditionally Symmetric Multidimensional Gaussian Mixtures in Genome-Wide Studies AI翻译
  作者：Sun, Ryan; Mccaw, Zachary R.; Lin, Xihong
  作者单位：University of Texas System; UTMD Anderson Cancer Center; Harvard University; Harvard T.H. Chan School of Public Health; Harvard University
  摘要：Causal mediation, pleiotropy, and replication analyses are three highly popular genetic study designs. Although these analyses address different scientific questions, the underlying statistical inference problems all involve large-scale testing of composite null hypotheses. The goal is to determine whether all null hypotheses-as opposed to at least one-in a set of individual tests should simultaneously be rejected. Recently, various methods have been proposed for each of these situations, incl...
• Estimating Higher-Order Mixed Memberships via the l2,∞ Tensor Perturbation Bound AI翻译
  作者：Agterberg, Joshua; Zhang, Anru R.
  作者单位：University of Illinois System; University of Illinois Urbana-Champaign; Duke University; Duke University; Duke University; Duke University
  摘要：Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it. In this article we propose the sub-Gaussian) tensor mixed-membership blockmodel, a generalization of the tensor blockmodel positing that memberships need not be discrete, but instead are convex combinations of latent communities. We establish the identifiability of our model...
• Monte Carlo Inference for Semiparametric Bayesian Regression AI翻译
  作者：Kowal, Daniel R.; Wu, Bohan
  作者单位：Cornell University; Rice University; Columbia University
  摘要：Data transformations are essential for broad applicability of parametric regression models. However, for Bayesian analysis, joint inference of the transformation and model parameters typically involves restrictive parametric transformations or nonparametric representations that are computationally inefficient and cumbersome for implementation and theoretical analysis, which limits their usability in practice. This article introduces a simple, general, and efficient strategy for joint posterior...
• Handbook of Bayesian, Fiducial, and Frequentist Inference AI翻译
  作者：Gu, Mengyang
  作者单位：University of California System; University of California Santa Barbara
• Objective Bayesian Inference AI翻译
  作者：Lee, Jaeyong
  作者单位：Seoul National University (SNU)
• An Adaptive Transfer Learning Framework for Functional Classification AI翻译
  作者：Qin, Caihong; Xie, Jinhan; Li, Ting; Bai, Yang
  作者单位：Shanghai University of Finance & Economics; Yunnan University
  摘要：In this article, we study the transfer learning problem in functional classification, aiming to improve the classification accuracy of the target data by leveraging information from related source datasets. To facilitate transfer learning, we propose a novel transferability function tailored for classification problems, enabling a more accurate evaluation of the similarity between source and target dataset distributions. Interestingly, we find that a source dataset can offer more substantial b...