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作者：Broderick, Tamara
作者单位：Massachusetts Institute of Technology (MIT)
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作者：Choi, David
作者单位：Carnegie Mellon University
摘要：Randomized experiments on social networks pose statistical challenges, due to the possibility of interference between units. We propose new methods for finding confidence intervals on the attributable treatment effect in such settings. The methods do not require partial interference, but instead require an identifying assumption that is similar to requiring nonnegative treatment effects. Network or spatial information can be used to customize the test statistic; in principle, this can increase...
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作者：Johnson, Valen E.; Payne, Richard D.; Wang, Tianying; Asher, Alex; Mandal, Soutrik
作者单位：Texas A&M University System; Texas A&M University College Station
摘要：Investigators from a large consortium of scientists recently performed a multi-year study in which they replicated 100 psychology experiments. Although statistically significant results were reported in 97% of the original studies, statistical significance was achieved in only 36% of the replicated studies. This article presents a reanalysis of these data based on a formal statistical model that accounts for publication bias by treating outcomes from unpublished studies as missing data, while ...
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作者：Konomi, Bledar A.; Karagiannis, Georgios; Lai, Kevin; Lin, Guang
作者单位：University System of Ohio; University of Cincinnati; Durham University; United States Department of Energy (DOE); Pacific Northwest National Laboratory; Purdue University System; Purdue University; Purdue University System; Purdue University; Purdue University in Indianapolis
摘要：In cases where field (or experimental) measurements are not available, computer models can model real physical or engineering systems to reproduce their outcomes. They are usually calibrated in light of experimental data to create a better representation of the real system. Statistical methods, based on Gaussian processes, for calibration and prediction have been especially important when the computer models are expensive and experimental data limited. In this article, we develop the Bayesian ...
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作者：Li, Jia; Todorov, Viktor; Tauchen, George
作者单位：Duke University; Northwestern University
摘要：We develop robust inference methods for studying linear dependence between the jumps of discretely observed processes at high frequency. Unlike classical linear regressions, jump regressions are determined by a small number of jumps occurring over a fixed time interval and the rest of the components of the processes around the jump times. The latter are the continuous martingale parts of the processes as well as observation noise. By sampling more frequently the role of these components, which...
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作者：Reiss, Philip T.; Goldsmith, Jeff
作者单位：University of Haifa; New York University; Columbia University
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作者：Blei, David M.; Kucukelbir, Alp; McAuliffe, Jon D.
作者单位：Columbia University; Columbia University; University of California System; University of California Berkeley
摘要：One of the core problems of modern statistics is to approximate difficult-to-compute probability densities. This problem is especially important in Bayesian statistics, which frames all inference about unknown quantities as a calculation involving the posterior density. In this article, we review variational inference (VI), a method from machine learning that approximates probability densities through optimization. VI has been used in many applications and tends to be faster than classical met...
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作者：Adusumilli, Karun; Otsu, Taisuke
作者单位：University of London; London School Economics & Political Science
摘要：In many statistical applications, the observed data take the form of sets rather than points. Examples include bracket data in survey analysis, tumor growth and rock grain images in morphology analysis, and noisy measurements on the support function of a convex set in medical imaging and robotic vision. Additionally, in studies of treatment effects, researchers often wish to conduct inference on nonparametric bounds for the effects which can be expressed by means of random sets. This article d...
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作者：Hof, Michel H.; Ravelli, Anita C.; Zwinderman, Aeilko H.
作者单位：University of Amsterdam; University of Amsterdam
摘要：In the absence of a unique identifier, combining information from multiple files relies on partially identifying variables (e.g., gender, initials). With a record linkage procedure, these variables are used to distinguish record pairs that belong together (matches) from record pairs that do not belong together (nonmatches). Generally, the combined strength of the partially identifying variables is too low causing imperfect linkage; some true nonmatches are identified as match and, on the other...
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作者：Lunagomez, Simon; Mukherjee, Sayan; Wolpert, Robert L.; Airoldi, Edoardo M.
作者单位：Harvard University; Duke University
摘要：We introduce a novel parameterization of distributions on hypergraphs based on the geometry of points in . The idea is to induce distributions on hypergraphs by placing priors on point configurations via spatial processes. This specification is then used to infer conditional independence models, or Markov structure, for multivariate distributions. This approach results in a broader class of conditional independence models beyond standard graphical models. Factorizations that cannot be retrieve...
