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作者：Rambachan, Ashesh; Roth, Jonathan
作者单位：Massachusetts Institute of Technology (MIT); Brown University
摘要：Design-based frameworks of uncertainty are frequently used in settings where the treatment is (conditionally) randomly assigned. This article develops a design-based framework suitable for analyzing quasi-experimental settings in the social sciences, in which the treatment assignment can be viewed as the realization of some stochastic process but there is concern about unobserved selection into treatment. In our framework, treatments are stochastic, but units may differ in their probabilities ...
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作者：Heng, Siyu; Zhang, Jiawei; Feng, Yang
作者单位：New York University; New York University; University of Chicago; New York University
摘要：Design-based causal inference, also known as randomization-based or finite-population causal inference, is one of the most widely used causal inference frameworks, largely due to the merit that its validity can be guaranteed by study design (e.g., randomized experiments) and does not require assuming specific outcome-generating distributions or super-population models. Despite its advantages, design-based causal inference can still suffer from other issues, among which outcome missingness is a...
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作者：Yu, Haihan; Kaiser, Mark S.; Nordman, Daniel J.
作者单位：University of Rhode Island; Iowa State University
摘要：The spectral density function can play a key role in time series analysis, where nonparametric interval estimation of the spectral density is a fundamental issue. However, the prevailing pointwise interval methods for spectral densities, including Chi-square approximation and frequency domain bootstrap (FDB), can be misleading in practice, perhaps more so than appreciated, as confidence intervals often exhibit low coverage accuracy as well as high sensitivity to tuning parameters. To provide a...
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作者：Ou, Rihui; Astfalck, Lachlan; Sen, Deborshee; Dunson, David
作者单位：Duke University; University of New South Wales Sydney; University of Western Australia
摘要：Bayesian computation often scales poorly with increasing data size, motivating developments such as divide-and-conquer approaches for scalable inference. These methods partition the data into subsets, perform parallel inference on each subset, and aggregate the results into a single posterior. Appealing theoretical properties and practical performance have been demonstrated for independent data; however, methods for dependent data remain challenging. Existing methods rely on ad hoc approximati...
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作者：Min, Yinjie; Peng, Liuhua; Zou, Changliang
作者单位：Nankai University; University of Melbourne; Nankai University
摘要：There is growing interest in constructing conformal prediction sets that provide approximate or asymptotic conditional coverage guarantees, capturing local data heterogeneity. However, methods like localized conformal prediction (LCP) may face challenges in ensuring reliable prediction sets in regions with sparse calibration data. This article introduces Enhanced Localized Conformal Prediction (ELCP), a novel approach that incorporates auxiliary data to refine localized prediction sets while p...
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作者：Zheng, Siming; Lin, Yuanyuan; Zhou, Yong; Huang, Jian
作者单位：Southeast University - China; Chinese University of Hong Kong; East China Normal University; Hong Kong Polytechnic University
摘要：We propose a domain-specific regression approach for domain generalization, taking into account possible heterogeneity among the datasets from different sources. In the proposed model, the domain-specific features are characterized through linear functionals of the marginal source distributions. The predictors are combined with the domain-specific linear functionals as inputs in the model. Using the source datasets, we estimate the domain-index function and the regression function nonparametri...
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作者：Bhadra, Subhankar; Pensky, Marianna; Sengupta, Srijan
作者单位：Pennsylvania Commonwealth System of Higher Education (PCSHE); Pennsylvania State University; Pennsylvania State University - University Park; State University System of Florida; University of Central Florida; North Carolina State University
摘要：Massive network datasets are becoming increasingly common in scientific applications. Existing community detection methods encounter significant computational challenges for such massive networks due to two reasons. First, the full network needs to be stored and analyzed on a single server, leading to high memory costs. Second, existing methods typically use matrix factorization or iterative optimization using the full network, resulting in high runtimes. We propose a strategy called predictiv...
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作者：Dukes, Oliver; Stensrud, Mats J.; Brioschi, Riccardo; Hudson, Aaron
作者单位：Ghent University; Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne; Fred Hutchinson Cancer Center
摘要：Recent work has focused on nonparametric estimation of conditional treatment effects, but inference has remained relatively unexplored. We propose a class of nonparametric tests for both quantitative and qualitative treatment effect heterogeneity. The tests can incorporate a variety of structured assumptions on the conditional average treatment effect, allow for both continuous and discrete covariates, and do not require sample splitting to obtain a tractable asymptotic null distribution. Furt...
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作者：Shang, Zuofeng; Sang, Peijun; Feng, Yang; Jin, Chong
作者单位：New Jersey Institute of Technology; University of Waterloo; New York University
摘要：We propose a functional stochastic block model whose vertices involve functional data information. This new model extends the classic stochastic block model with vector-valued nodal information, and finds applications in real-world networks whose nodal information could be functional curves. Examples include international trade data in which a network vertex (country) is associated with the annual or quarterly GDP over a certain time period, and MyFitnessPal data in which a network vertex (MyF...
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作者：Frazier, David T.; Nott, David J.
作者单位：Monash University; National University of Singapore; National University of Singapore
摘要：Modular Bayesian methods perform inference in models that are specified through a collection of coupled sub-models, known as modules. These modules often arise from modeling different data sources or from combining domain knowledge from different disciplines. Cutting feedback is a Bayesian inference method that ensures misspecification of one module does not affect inferences for parameters in other modules, and produces what is known as the cut posterior. However, choosing between the cut pos...
