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作者：Dominitz, Jeff; Manski, Charles F.
作者单位：Rice University; Northwestern University; Northwestern University
摘要：The potential impact of non-sampling errors on election polls is well known, but measurement has focused on the margin of sampling error. Statisticians have recommended measurement of total survey error by mean square error (MSE), which jointly measures sampling and non-sampling errors. We suggest use of the square root of maximum MSE to measure the total margin of error (TME). We suggest that measurement of TME should be a standard feature in the reporting of polls. Because the exceedingly lo...
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作者：Gronsbell, Jessica
作者单位：University of Toronto
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作者：Cheng, Gang; Chen, Yen-Chi; Unger, Joseph M.; Till, Cathee; Zhao, Ying-Qi
作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center
摘要：Combining experimental and observational follow-up datasets has received much attention lately. In a survival setting, recent work has used Medicare claims to extend the follow-up period for participants in a prostate cancer clinical trial. This allows the estimation of the long-term effect that cannot be estimated by the trial data alone. In this article, we study the estimation of long-term effect when participants in a clinical trial are linked to an observational follow-up dataset. Such li...
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作者：Qi, Zhengling; Bai, Chenjia; Wang, Zhaoran; Wang, Lan
作者单位：George Washington University; Harbin Institute of Technology; Northwestern University; University of Miami; China Telecom Corp. Ltd.
摘要：In the literature of reinforcement learning (RL), off-policy evaluation is mainly focused on estimating a value of a target policy given the pre-collected data generated by some behavior policy. Motivated by the recent success of distributional RL in many practical applications, we study the distributional off-policy evaluation problem in the batch setting when the reward is multi-variate. We propose an offline Wasserstein-based approach to simultaneously estimate the joint distribution of a m...
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作者：Zhang, Bo; Gao, Jiti; Pan, Guangming; Yang, Yanrong
作者单位：Chinese Academy of Sciences; University of Science & Technology of China, CAS; Monash University; Nanyang Technological University; Australian National University
摘要：Cross-sectional structures and temporal tendency are important features of high-dimensional time series. Based on eigen-analysis on sample covariance matrices, we propose a novel approach to identifying four popular structures of high-dimensional time series, which are grouped in terms of factor structures and stationarity. The proposed three-step method includes: a ratio statistic of empirical eigenvalues; a projected Augmented Dickey-Fuller Test; a new unit-root test based on the largest emp...
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作者：Beraha, Mario; Favaro, Stefano; Sesia, Matteo
作者单位：University of Milano-Bicocca; University of Turin; Collegio Carlo Alberto; University of Southern California; University of Southern California
摘要：We provide a novel statistical perspective on a classical problem at the intersection of computer science and information theory: recovering the empirical frequency of a symbol in a large discrete dataset using only a compressed representation, or sketch, obtained via random hashing. Departing from traditional algorithmic approaches, recent works have proposed Bayesian nonparametric (BNP) methods that can provide more informative frequency estimates by leveraging modeling assumptions about the...
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作者：Ting, Angela; Linero, Antonio R.
作者单位：University of Texas System; University of Texas Austin
摘要：The causal inference literature has increasingly recognized that targeting treatment effect heterogeneity can lead to improved scientific understanding and policy recommendations. Similarly, studying the causal pathway connecting the treatment to the outcome can be useful. We address these problems in the context of causal mediation analysis. We introduce a varying coefficient model based on Bayesian additive regression trees to estimate and regularize heterogeneous causal mediation effects. E...
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作者：Yu, Dalei; Zhang, Xinyu; Liang, Hua
作者单位：Xi'an Jiaotong University; Chinese Academy of Sciences; University of Science & Technology of China, CAS; Chinese Academy of Sciences; George Washington University
摘要：Studying unified model averaging estimation for situations with complicated data structures, we propose a novel model averaging method based on cross-validation (MACV). MACV unifies a large class of new and existing model averaging estimators and covers a very general class of loss functions. Furthermore, to reduce the computational burden caused by the conventional leave-subject/one-out cross-validation, we propose a SEcond-order-Approximated Leave-one/subject-out (SEAL) cross-validation, whi...
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作者：Li, Sijia; Gilbert, Peter B.; Duan, Rui; Luedtke, Alex
作者单位：University of Washington; University of Washington Seattle; Fred Hutchinson Cancer Center; Harvard University; Harvard T.H. Chan School of Public Health; University of Washington; University of Washington Seattle
摘要：We introduce a new data fusion method that uses multiple data sources to estimate a smooth, finite-dimensional parameter. Most existing methods only make use of fully aligned data sources that share common conditional distributions of one or more variables of interest. However, in many settings, the scarcity of fully aligned sources can make existing methods require unduly large sample sizes to be useful. Our approach enables the incorporation of weakly aligned data sources that are not perfec...
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作者：Lei, Jing; Oliveira, Natalia L.; Tibshirani, Ryan J.
作者单位：Carnegie Mellon University; Alphabet Inc.; Google Incorporated; University of California System; University of California Berkeley
