551期

最新文章

• Dirichlet-Laplace priors for optimal shrinkage (vol 110, pg1479-1490, 2014 ) AI翻译
  作者：Gruber, Luis; Kastner, Gregor; Bhattacharya, Anirban; Pati, Debdeep; Pillai, Natesh; Dunson, David
  作者单位：University of Klagenfurt; Texas A&M University System; Texas A&M University College Station; University of Wisconsin System; University of Wisconsin Madison; Harvard University; Duke University
  摘要：Bhattacharya et al. introduce a novel prior, the Dirichlet-Laplace (DL) prior, and propose a Markov chain Monte Carlo (MCMC) method to simulate posterior draws under this prior in a conditionally Gaussian setting. The original algorithm samples from conditional distributions in the wrong order, that is, it does not correctly sample from the joint posterior distribution of all latent variables. This note details the issue and provides two simple solutions: A correction to the original algorithm...
• Deconvolution Density Estimation with Penalized MLE AI翻译
  作者：Cai, Yun; Gu, Hong; Kenney, Toby
  作者单位：Dalhousie University
  摘要：Deconvolution is the important problem of estimating the distribution of a quantity of interest from a sample with additive measurement error. Nearly all infinite-dimensional deconvolution methods in the literature use Fourier transformations. These methods are mathematically neat, but unstable, and produce bad estimates when signal-noise ratio or sample size are low. A popular alternative is to maximize penalized likelihood for a finite-dimensional basis expansion of the unknown density. We d...
• Generalized Linear Mixed Models: Modern Concepts, Methods and Applications, 2nd ed. AI翻译
  作者：Liu, Xing
  作者单位：Connecticut State University System; Eastern Connecticut State University
• Statistical Inference for High-Dimensional Spectral Density Matrix AI翻译
  作者：Chang, Jinyuan; Jiang, Qing; Mcelroy, Tucker; Shao, Xiaofeng
  作者单位：Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China; Chinese Academy of Sciences; Nanjing Institute of Geology & Paleontology, CAS; Academy of Mathematics & System Sciences, CAS; Beijing Normal University; Washington University (WUSTL); Washington University (WUSTL)
  摘要：The spectral density matrix is a fundamental object of interest in time series analysis, and it encodes both contemporary and dynamic linear relationships between component processes of the multivariate system. In this article we develop novel inference procedures for the spectral density matrix in the high-dimensional setting. Specifically, we introduce a new global testing procedure to test the nullity of the cross-spectral density for a given set of frequencies and across pairs of component...
• Identifying Genetic Variants for Brain Connectivity Using Ball Covariance Ranking and Aggregation AI翻译
  作者：Dai, Wei; Zhang, Heping
  作者单位：Yale University
  摘要：Understanding the genetic architecture of brain functions is essential to clarify the biological etiologies of behavioral and psychiatric disorders. Functional connectivity, representing pairwise correlations of neural activities between brain regions, is moderately heritable. Current methods to identify single nucleotide polymorphisms (SNPs) linked to functional connectivity either neglect the complex structure of functional connectivity or fail to control false discoveries. Therefore, we pro...
• Financial Data Analytics with R: Monte-Carlo Validation AI翻译
  作者：Sit, Tony
  作者单位：Chinese University of Hong Kong
• Statistical Prediction and Machine Learning AI翻译
  作者：Pesta, Michal
  作者单位：Charles University Prague
• Asymptotic Guarantees for Bayesian Phylogenetic Tree Reconstruction AI翻译
  作者：Kirichenko, Alisa; Kelly, Luke J.; Koskela, Jere
  作者单位：University of Warwick; University College Cork; Newcastle University - UK
  摘要：We derive tractable criteria for the consistency of Bayesian tree reconstruction procedures, which constitute a central class of algorithms for inferring common ancestry among DNA sequence samples in phylogenetics. Our results encompass several Bayesian algorithms in widespread use, such as BEAST, MrBayes, and RevBayes. Unlike essentially all existing asymptotic guarantees for tree reconstruction, we require no discretization or boundedness assumptions on branch lengths. Our results are also v...
• Network Goodness-of-Fit for the Block-Model Family AI翻译
  作者：Jin, Jiashun; Ke, Zheng Tracy; Tang, Jiajun; Wang, Jingming
  作者单位：Carnegie Mellon University; Harvard University; University of Virginia
  摘要：The block-model family has four popular network models (SBM, DCBM, MMSBM, and DCMM). A fundamental problem is, how well each of these models fits with real networks. We propose GoF-MSCORE as a new Goodness-of-Fit (GoF) metric for DCMM (the broadest one among the four), with two main ideas. The first is to use cycle count statistics as a general recipe for GoF. The second is a novel network fitting scheme. GoF-MSCORE is a flexible GoF approach, and we further extend it to SBM, DCBM, and MMSBM. ...
• Who Are We Missing?: A Principled Approach to Characterizing the Underrepresented Population AI翻译
  作者：Parikh, Harsh; Ross, Rachael K.; Stuart, Elizabeth; Rudolph, Kara E.
  作者单位：Johns Hopkins University; Columbia University
  摘要：Randomized controlled trials (RCTs) serve as the cornerstone for understanding causal effects, yet extending inferences to target populations presents challenges due to effect heterogeneity and underrepresentation. Our article addresses the critical issue of identifying and characterizing underrepresented subgroups in RCTs, proposing a novel framework for refining target populations to improve generalizability. We introduce an optimization-based approach, Rashomon Set of Optimal Trees (ROOT), ...