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作者：Choe, Yo Joong; Ramdas, Aaditya
作者单位：INSEAD Business School; Carnegie Mellon University
摘要：In sequential anytime-valid inference, any admissible procedure must be based on e-processes: generalizations of test martingales that quantify the accumulated evidence against a composite null hypothesis at any stopping time. This paper proposes a method for combining e-processes constructed in different filtrations but for the same null. Although e-processes in the same filtration can be combined effortlessly (by averaging), e-processes in different filtrations cannot because their validity ...
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作者：Choe, Yo Joong; Ramdas, Aaditya
作者单位：INSEAD Business School; Carnegie Mellon University
摘要：In sequential anytime-valid inference, any admissible procedure must be based on e-processes: generalizations of test martingales that quantify the accumulated evidence against a composite null hypothesis at any stopping time. This paper proposes a method for combining e-processes constructed in different filtrations but for the same null. Although e-processes in the same filtration can be combined effortlessly (by averaging), e-processes in different filtrations cannot because their validity ...
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作者：Koning, Nick W.; Van Meer, Sam
作者单位：Erasmus University Rotterdam - Excl Erasmus MC; Erasmus University Rotterdam
摘要：Anytime valid sequential tests permit us to stop testing based on the current data, without invalidating the inference. Given a maximum number of observations N, one may believe this must come at the cost of power when compared to a conventional test that waits until all N observations have arrived. Our first contribution is to show that this is false: for any valid test based on N observations, we show how to construct an anytime valid sequential test that matches it after N observations. Our...
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作者：Deng, Daxuan; Han, Peisong; Chen, Shuo; Wang, Ming; Chen, Chixiang
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Penn State Health; Gilead Sciences; University System of Maryland; University of Maryland Baltimore; University System of Ohio; Case Western Reserve University
摘要：In the era of big data, secondary outcomes have become increasingly important alongside primary outcomes. These secondary outcomes, which can be derived from traditional endpoints in clinical trials, compound measures, or risk prediction scores, hold the potential to enhance the analysis of primary outcomes. Our method is motivated by the challenge of utilizing multiple secondary outcomes, such as blood biochemistry markers and urine assays, to improve the analysis of the primary outcome relat...
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作者：Du, Jinye; Wang, Qihua
作者单位：Chinese Academy of Sciences; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS
摘要：Empirical likelihood encounters serious computational challenges when applied to massive datasets or multiple data sources distributed across decentralized networks. This paper proposes a constrained empirical likelihood framework for decentralized networks, utilizing a novel penalization technique to obtain a penalized empirical log-likelihood. The resulting empirical log-likelihood ratio statistic is proved to be asymptotically standard chi-squared even for a divergent machine number. Howeve...
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作者：Wei, Song; Xie, Yao
作者单位：University System of Georgia; Georgia Institute of Technology
摘要：We present a computationally efficient online kernel Cumulative Sum method for change-point detection that utilizes the maximum over a set of kernel statistics to account for the unknown change-point location. Our approach exhibits increased sensitivity to small changes compared to existing kernel-based change-point detection methods, including the Scan-B statistic, corresponding to a non-parametric Shewhart chart-type procedure. We provide accurate analytic approximations for two key performa...
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作者：Cavaliere, Giuseppe; Mikosch, Thomas; Rahbek, Anders; Vilandt, Frederik
作者单位：University of Bologna; University of Exeter; University of Copenhagen; University of Copenhagen
摘要：Integrated autoregressive conditional duration (ACD) models serve as counterparts to integrated generalized autoregressive conditional heteroskedastic models used for financial returns. However, despite their resemblance, asymptotic theory for ACD is still incomplete. Central challenges arise from the facts that (i) integrated ACD processes imply durations with infinite expectation and (ii) conventional asymptotic approaches break down due to the randomness in the number of durations within a ...
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作者：McClean, Alec; Balakrishnan, Sivaraman; Kennedy, Edward H.; Wasserman, Larry
作者单位：Carnegie Mellon University
摘要：Double cross-fit doubly robust (DCDR) estimators, which train nuisance function estimators on separate samples, are effective new estimators for causal functionals. We establish several novel theoretical results for them, building on recent work. We provide a structure-agnostic error analysis, which holds with generic nuisance functions and estimators. Then, we propose n-consistent DCDR estimators with undersmoothed local polynomial regression and k-Nearest Neighbours and a minimax rate-optima...
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作者：Chen, Xinyuan; Li, Fan
作者单位：Mississippi State University; Yale University; Yale University
摘要：Principal stratification is a popular framework for causal inference in the presence of an intermediate outcome. While the principal average treatment effects are the standard target of inference, they may be insufficient when interest lies in the relative ordering of potential outcomes within a principal stratum. We introduce the principal generalized causal effect estimands to accommodate nonlinear contrast functions, providing robust, probability-scale summaries suitable for ordinal outcome...
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作者：Hudson, Aaron; Carone, Marco; Shojaie, Ali
作者单位：Fred Hutchinson Cancer Center; University of Washington; University of Washington Seattle
摘要：It is often of interest to make inference on an unknown function that is a local parameter of the data-generating mechanism, such as a density or regression function. Such estimands can typically only be estimated at a slower-than-parametric rate in nonparametric and semiparametric models, and performing calibrated inference can be challenging. In many cases, these estimands can be expressed as the minimizer of a population risk functional. Here, we propose a general framework that leverages s...
