539期

最新文章

• 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...
• 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
• Modeling High-Dimensional Time Series: A Factor Model With Dynamically Dependent Factors and Diverging Eigenvalues AI翻译
  作者：Gao, Zhaoxing; Tsay, Ruey S.
  作者单位：Lehigh University; University of Chicago
  摘要：This article proposes a new approach to modeling high-dimensional time series by treating a p-dimensional time series as a nonsingular linear transformation of certain common factors and idiosyncratic components. Unlike the approximate factor models, we assume that the factors capture all the nontrivial dynamics of the data, but the cross-sectional dependence may be explained by both the factors and the idiosyncratic components. Under the proposed model, (a) the factor process is dynamically d...
• Comment on: Confidence Intervals for Nonparametric Empirical Bayes Analysis by Ignatiadis and Wager AI翻译
  作者：Imbens, Guido
  作者单位：Stanford University; Stanford University; National Bureau of Economic Research
• Comments on Measuring Housing Vitality from Multi-Source Big Data and Machine Learning AI翻译
  作者：Tu, Wei; Jiang, Bei; Kong, Linglong
  作者单位：Queens University - Canada; Queens University - Canada; University of Alberta
• Copula Regression for Compound Distributions with Endogenous Covariates with Applications in Insurance Deductible Pricing AI翻译
  作者：Shi, Peng; Lee, Gee Y.
  作者单位：University of Wisconsin System; University of Wisconsin Madison; Michigan State University
  摘要：This article concerns deductible pricing in nonlife insurance contracts. The primary interest of insurers is the effect of the contract deductible on a policyholder's aggregate loss that is determined by a compound distribution where the sum of individual claim amount is stopped by the number of claims. Policyholders choose the deductible level based on their hidden risks, which makes deductible endogenous in the regressions for both claim frequency and claim severity. To address the endogenei...
• Confidence Intervals for Nonparametric Empirical Bayes Analysis AI翻译
  作者：Ignatiadis, Nikolaos; Wager, Stefan
  作者单位：Stanford University; Stanford University
  摘要：In an empirical Bayes analysis, we use data from repeated sampling to imitate inferences made by an oracle Bayesian with extensive knowledge of the data-generating distribution. Existing results provide a comprehensive characterization of when and why empirical Bayes point estimates accurately recover oracle Bayes behavior. In this paper, we develop flexible and practical confidence intervals that provide asymptotic frequentist coverage of empirical Bayes estimands, such as the posterior mean ...
• Covariate Information Number for Feature Screening in Ultrahigh-Dimensional Supervised Problems AI翻译
  作者：Nandy, Debmalya; Chiaromonte, Francesca; Li, Runze
  作者单位：Colorado School of Public Health; University of Colorado System; University of Colorado Anschutz Medical Campus; Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; Scuola Superiore Sant'Anna; Scuola Superiore Sant'Anna
  摘要：Contemporary high-throughput experimental and surveying techniques give rise to ultrahigh-dimensional supervised problems with sparse signals; that is, a limited number of observations (n), each with a very large number of covariates (p >> n), only a small share of which is truly associated with the response. In these settings, major concerns on computational burden, algorithmic stability, and statistical accuracy call for substantially reducing the feature space by eliminating redundant covar...