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作者：Koenig, Claudia; Munk, Axel; Werner, Frank
作者单位：University of Gottingen; University of Gottingen; University of Wurzburg
摘要：We develop a multiscale scanning method to find anomalies in a d-dimensional random field in the presence of nuisance parameters. This covers the common situation that either the baseline-level or additional parameters such as the variance are unknown and have to be estimated from the data. We argue that state of the art approaches to determine asymptotically correct critical values for multiscale scanning statistics will in general fail when such parameters are naively replaced by plug-in est...
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作者：Bellec, Pierre C.; Du, Jin-Hong; Koriyama, Takuya; Patil, Pratik; Tan, Kai
作者单位：Rutgers University System; Rutgers University New Brunswick; Carnegie Mellon University; Carnegie Mellon University; University of Chicago; University of California System; University of California Berkeley
摘要：Generalized cross-validation (GCV) is a widely used method for estimating the squared out-of-sample prediction risk that employs scalar degrees of freedom adjustment (in a multiplicative sense) to the squared training error. In this paper, we examine the consistency of GCV for estimating the prediction risk of arbitrary ensembles of penalized least-squares estimators. We show that GCV is inconsistent for any finite ensemble of size greater than one. Towards repairing this shortcoming, we ident...
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作者：Jin, Ying; Ren, Zhimei
作者单位：Harvard University; University of Pennsylvania
摘要：Conformal prediction builds marginally valid prediction intervals that cover the unknown outcome of a randomly drawn test point with a prescribed probability. However, in practice, data-driven methods are often used to identify specific test unit(s) of interest, requiring uncertainty quantification tailored to these focal units. In such cases, marginally valid conformal prediction intervals may fail to provide valid coverage for the focal unit(s) due to selection bias. This article presents a ...
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作者：Zhang, Chenlin; Zhou, Ling; Guo, Bin; Lin, Huazhen
作者单位：Southwestern University of Finance & Economics - China; Southwestern University of Finance & Economics - China
摘要：We develop a Spatial Effect Detection Regression (SEDR) model to capture the nonlinear and irregular effects of high-dimensional spatio-temporal predictors on a scalar outcome. Specifically, we assume that both the component and the coefficient functions in the SEDR are unknown smooth functions of location and time. This allows us to leverage spatially and temporally correlated information, transforming the curse of dimensionality into a blessing, as confirmed by our theoretical and numerical ...
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作者：Luedtke, Alex
作者单位：University of Washington; University of Washington Seattle
摘要：We introduce an algorithm that simplifies the construction of efficient estimators, making them accessible to a broader audience. 'Dimple' takes as input computer code representing a parameter of interest and outputs an efficient estimator. Unlike standard approaches, it does not require users to derive a functional derivative known as the efficient influence function. Dimple avoids this task by applying automatic differentiation to the statistical functional of interest. Doing so requires exp...
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作者：Singh, Rahul; Iliopoulos, George; Davidov, Ori
作者单位：Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Delhi; University of Piraeus; University of Haifa
摘要：Least square estimators for graphical models for cardinal paired comparison data with and without covariates are rigorously analysed. Novel, graph-based, necessary, and sufficient conditions that guarantee strong consistency, asymptotic normality, and the exponential convergence of the estimated ranks are emphasized. A complete theory for models with covariates is laid out. In particular, conditions under which covariates can be safely omitted from the model are provided. The methodology is em...
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作者：Fischer, Lasse; Ramdas, Aaditya
作者单位：University of Bremen; Carnegie Mellon University
摘要：In a Monte Carlo test, the observed dataset is fixed, and several resampled or permuted versions of the dataset are generated in order to test a null hypothesis that the original dataset is exchangeable with the resampled/permuted ones. Sequential Monte Carlo tests aim to save computational resources by generating these additional datasets sequentially one by one and potentially stopping early. While earlier tests yield valid inference at a particular prespecified stopping rule, our work devel...
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作者：Rosenbaum, Paul R.
作者单位：University of Pennsylvania
摘要：In an observational block design, there are I blocks of J individuals, typically with one treated individual and J-1 controls; however, unlike a randomized block design, individuals were not randomly assigned to treatment or control. To be convincing, an observational block design must demonstrate that an ostensible treatment effect is not actually a consequence of small or moderate unmeasured biases of treatment assignment in the absence of a treatment effect. It is known that weighting to ig...
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作者：Duarte, Eliana; Solus, Liam
作者单位：Universidade do Porto; Royal Institute of Technology
摘要：We address the problem of representing context-specific causal models based on both observational and experimental data collected under general (e.g. hard or soft) interventions by introducing a new family of context-specific conditional independence models called CStrees. This family is defined via a novel factorization criterion that allows for a generalization of the factorization property defining general interventional directed acyclic graph (DAG) models. We derive a graphical characteriz...
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作者：Bucher, Axel; Staud, Torben
作者单位：Ruhr University Bochum
摘要：The block maxima method is a standard approach for analyzing the extremal behaviour of a potentially multivariate time series. It has recently been found that the classical approach based on disjoint block maxima may be universally improved by considering sliding block maxima instead. However, the asymptotic variance formula for estimators based on sliding block maxima involves an integral over the covariance of a certain family of multivariate extreme value distributions, which makes its esti...
