2022

最新文章

• From Fixed-X to Random-X Regression: Bias-Variance Decompositions, Covariance Penalties, and Prediction Error Estimation (vol 115, pg 138, 2020) AI翻译
  作者：Rosset, Saharon; Tibshirani, Ryan J.
  作者单位：Tel Aviv University; Carnegie Mellon University
• Modeling Time-Varying Random Objects and Dynamic Networks AI翻译
  作者：Dubey, Paromita; Muller, Hans-Georg
  作者单位：Stanford University; University of California System; University of California Davis
  摘要：Samples of dynamic or time-varying networks and other random object data such as time-varying probability distributions are increasingly encountered in modern data analysis. Common methods for time-varying data such as functional data analysis are infeasible when observations are time courses of networks or other complex non-Euclidean random objects that are elements of general metric spaces. In such spaces, only pairwise distances between the data objects are available and a strong limitation...
• Minimax Efficient Random Experimental Design Strategies With Application to Model-Robust Design for Prediction AI翻译
  作者：Waite, Timothy W.; Woods, David C.
  作者单位：University of Manchester; University of Southampton
  摘要：In game theory and statistical decision theory, a random (i.e., mixed) decision strategy often outperforms a deterministic strategy in minimax expected loss. As experimental design can be viewed as a game pitting the Statistician against Nature, the use of a random strategy to choose a design will often be beneficial. However, the topic of minimax-efficient random strategies for design selection is mostly unexplored, with consideration limited to Fisherian randomization of the allocation of a ...
• High-Dimensional Vector Autoregressive Time Series Modeling via Tensor Decomposition AI翻译
  作者：Wang, Di; Zheng, Yao; Lian, Heng; Li, Guodong
  作者单位：University of Hong Kong; University of Connecticut; City University of Hong Kong
  摘要：The classical vector autoregressive model is a fundamental tool for multivariate time series analysis. However, it involves too many parameters when the number of time series and lag order are even moderately large. This article proposes to rearrange the transition matrices of the model into a tensor form such that the parameter space can be restricted along three directions simultaneously via tensor decomposition. In contrast, the reduced-rank regression method can restrict the parameter spac...
• Functional Sequential Treatment Allocation AI翻译
  作者：Kock, Anders Bredahl; Preinerstorfer, David; Veliyev, Bezirgen
  作者单位：University of Oxford; Aarhus University; CREATES; Universite Libre de Bruxelles
  摘要：Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome before the next subject arrives. Initially, it is unknown which treatment is best, but the sequential nature of the problem permits learning about the effectiveness of the treatments. While the multi-armed-bandit literature has shed much light on the situation when the policy maker compares the effectiveness of the treatments through their mean, much less is known about other targets. This is rest...
• Highly Scalable Bayesian Geostatistical Modeling via Meshed Gaussian Processes on Partitioned Domains AI翻译
  作者：Peruzzi, Michele; Banerjee, Sudipto; Finley, Andrew O.
  作者单位：Michigan State University; Duke University; University of California System; University of California Los Angeles
  摘要：We introduce a class of scalable Bayesian hierarchical models for the analysis of massive geostatistical datasets. The underlying idea combines ideas on high-dimensional geostatistics by partitioning the spatial domain and modeling the regions in the partition using a sparsity-inducing directed acyclic graph (DAG). We extend the model over the DAG to a well-defined spatial process, which we call the meshed Gaussian process (MGP). A major contribution is the development of an MGPs on tessellate...
• Mean and Covariance Estimation for Functional Snippets AI翻译
  作者：Lin, Zhenhua; Wang, Jane-Ling
  作者单位：National University of Singapore; University of California System; University of California Davis
  摘要：We consider estimation of mean and covariance functions of functional snippets, which are short segments of functions possibly observed irregularly on an individual specific subinterval that is much shorter than the entire study interval. Estimation of the covariance function for functional snippets is challenging since information for the far off-diagonal regions of the covariance structure is completely missing. We address this difficulty by decomposing the covariance function into a varianc...
• Estimation of Subgraph Densities in Noisy Networks AI翻译
  作者：Chang, Jinyuan; Kolaczyk, Eric D.; Yao, Qiwei
  作者单位：Southwestern University of Finance & Economics - China; Boston University; University of London; London School Economics & Political Science
  摘要：While it is common practice in applied network analysis to report various standard network summary statistics, these numbers are rarely accompanied by uncertainty quantification. Yet any error inherent in the measurements underlying the construction of the network, or in the network construction procedure itself, necessarily must propagate to any summary statistics reported. Here we study the problem of estimating the density of an arbitrary subgraph, given a noisy version of some underlying n...
• Discussion of Confidence Intervals for Nonparametric Empirical Bayes Analysis AI翻译
  作者：Pensky, Marianna
  作者单位：State University System of Florida; University of Central Florida
• Comment on Factor Models for High-Dimensional Tensor Time Series AI翻译
  作者：Pena, Daniel
  作者单位：Universidad Carlos III de Madrid; Universidad Carlos III de Madrid