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作者：Cao, Jian; Katzfuss, Matthias
作者单位：University of Houston System; University of Houston; University of Wisconsin System; University of Wisconsin Madison
摘要：Multivariate normal (MVN) probabilities arise in myriad applications, but they are analytically intractable and need to be evaluated via Monte Carlo-based numerical integration. For the state-of-the-art minimax exponential tilting (MET) method, we show that the complexity of each of its components can be greatly reduced through an integrand parameterization that uses the sparse inverse Cholesky factor produced by the Vecchia approximation, whose approximation error is often negligible relative...
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作者：He, Chenxuan; Chen, Canyi; Zhu, Liping
作者单位：Renmin University of China; University of Michigan System; University of Michigan
摘要：Black-box learners have demonstrated remarkable success across various fields due to their high predictive accuracy. However, the complexity of their learning procedures poses significant challenges in evaluating whether a given learner has achieved optimal performance on datasets with unknown data-generating mechanisms. We propose a general goodness-of-fit test for assessing different learning procedures involving high-dimensional predictors, encompassing methods from classical linear regress...
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作者：Garcia-Portugues, Eduardo; Meilan-Vila, Andrea
作者单位：Universidad Carlos III de Madrid
摘要：A kernel density estimator for data on the polysphere S(d1)x & ctdot;xS(dr), with r,d(1),& mldr;,d(r)>= 1, is presented in this article. We derive the main asymptotic properties of the estimator, including mean square error, normality, and optimal bandwidths. We address the kernel theory of the estimator beyond the von Mises-Fisher kernel, introducing new kernels that are more efficient and investigating normalizing constants, moments, and sampling methods thereof. Plug-in and cross-validated ...
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作者：Liao, Yuan; Todorov, Viktor
摘要：We test for temporal stability in local linear projection coefficients of observable factors on latent ones embedded in a high-dimensional vector obeying a linear factor model. The proposed test explores the fact that, under the null hypothesis, residuals from global linear projections of observable factors on latent ones, computed over a fixed time interval via Principal Component Analysis (PCA), should also be locally uncorrelated with the PCA factors. The test is fully nonparametric. Its as...
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作者：Wu, Peikai; Xiao, Zhiguo
作者单位：Fudan University
摘要：We introduce a novel framework for causal inference in dynamic panel data that extends the cross-sectional Double/Debiased Machine Learning (DML) approach. The method rests on a partially linear dynamic panel model with two-way fixed effects for potential outcomes and readily accommodates binary, multi-valued, or continuous treatments. To translate potential outcome models into an estimable form, we introduce dynamic conditional independence assumptions. By integrating cross-fitting, the nonpa...
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作者：Dalla Pria, Marco; Ruggiero, Matteo; Spano, Dario
作者单位：University of Turin; New York University; New York University Abu Dhabi; University of Warwick
摘要：We introduce a nonparametric model for inferring time-evolving, unobserved probability distributions from discrete-time data consisting of unlabelled partitions. The latent process is a two-parameter Poisson-Dirichlet diffusion, and observations arise via exchangeable sampling. Applications include social and genetic data where only aggregate clustering summaries are observed. To address the intractable likelihood, we develop a tractable inferential framework that avoids label enumeration and ...
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作者：Liang, Ruiting; Zhu, Wanrong; Barber, Rina Foygel
作者单位：University of Chicago; University of California System; University of California Irvine; University of Chicago
摘要：Given a family of pretrained models and a hold-out set, how can we construct a valid conformal prediction set while selecting a model that minimizes the width of the set? If we use the same hold-out dataset both to select a model (the model that yields the smallest conformal prediction sets) and then to construct a conformal prediction set based on that selected model, we suffer a loss of coverage due to selection bias. Alternatively, we could further split the data to perform selection and ca...
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作者：Chen, Zhe; Li, Xinran
作者单位：University of Pennsylvania; Pennsylvania Medicine; University of Chicago
摘要：Understanding treatment effect heterogeneity has become increasingly important in many fields. In this article we study distributions and quantiles of individual treatment effects to provide a more comprehensive and robust understanding of treatment effects beyond usual averages, although they are more challenging to infer due to nonidentifiability from the observed data. Recent randomization-based approaches offer finite-sample valid inference for treatment effect distributions and quantiles ...
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作者：Rosenbaum, Paul R.
作者单位：University of Pennsylvania
摘要：Does light daily alcohol consumption lengthen life? In any observational study of this question, we expect drinking behavior-that is, treatment assignment-to be confounded by measured and unmeasured covariates. An observational study has two evidence factors if there are two essentially independent tests of the same null hypothesis about causal effects, where the two tests are susceptible to different unmeasured biases in treatment assignment. Because they are independent, two evidence factors...
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作者：Harshaw, Christopher; Middleton, Joel; Savje, Fredrik
作者单位：Columbia University; Uppsala University; Uppsala University
摘要：Unbiased and consistent variance estimators generally do not exist for design-based treatment effect estimators because experimenters never observe more than one potential outcome for any unit. The problem is exacerbated by interference and complex experimental designs. Experimenters must accept conservative variance estimators in these settings, but they can strive to minimize the conservativeness. In this article, we show that the task of constructing a minimally conservative variance estima...
