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作者：Kang, Hyunseung; Kreuels, Benno; May, Juergen; Small, Dylan S.
作者单位：University of Pennsylvania; University of Hamburg; University Medical Center Hamburg-Eppendorf; Leibniz Association; Bernhard Nocht Institut fur Tropenmedizin
摘要：Most previous studies of the causal relationship between malaria and stunting have been studies where potential confounders are controlled via regression-based methods, but these studies may have been biased by unobserved confounders. Instrumental variables (IV) regression offers a way to control for unmeasured confounders where, in our case, the sickle cell trait can be used as an instrument. However, for the instrument to be valid, it may still be important to account for measured confounder...
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作者：Kuusela, Mikael; Panaretos, Victor M.
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：We consider the high energy physics unfolding problem where the goal is to estimate the spectrum of elementary particles given observations distorted by the limited resolution of a particle detector. This important statistical inverse problem arising in data analysis at the Large Hadron Collider at CERN consists in estimating the intensity function of an indirectly observed Poisson point process. Unfolding typically proceeds in two steps: one first produces a regularized point estimate of the ...
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作者：Maroulas, Vasileios; Nebenfuehr, Andreas
作者单位：University of Tennessee System; University of Tennessee Knoxville; University of Tennessee System; University of Tennessee Knoxville
摘要：We focus on the biological problem of tracking organelles as they move through cells. In the past, most intracellular movements were recorded manually, however, the results are too incomplete to capture the full complexity of organelle motions. An automated tracking algorithm promises to provide a complete analysis of noisy microscopy data. In this paper, we adopt statistical techniques from a Bayesian random set point of view. Instead of considering each individual organelle, we examine a ran...
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作者：Wang, Zhishi; He, Qiuling; Larget, Bret; Newton, Michael A.
作者单位：University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
摘要：We develop a model-based methodology for integrating gene-set information with an experimentally-derived gene list. The methodology uses a previously reported samplingmodel, but takes advantage of natural constraints in the high-dimensional discrete parameter space in order to work from a more structured prior distribution than is currently available. We show how the natural constraints are expressed in terms of linear inequality constraints within a set of binary latent variables. Further, th...
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作者：Asadi, Peiman; Davison, Anthony C.; Engelke, Sebastian
作者单位：Swiss Federal Institutes of Technology Domain; Ecole Polytechnique Federale de Lausanne
摘要：Max-stable processes are the natural extension of the classical extreme-value distributions to the functional setting, and they are increasingly widely used to estimate probabilities of complex extreme events. In this paper we broaden them from the usual situation in which dependence varies according to functions of Euclidean distance to situations in which extreme river discharges at two locations on a river network may be dependent because the locations are flow-connected or because of commo...
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作者：Schofield, Lynne Steuerle
作者单位：Swarthmore College
摘要：This paper represents a methodological-substantive synergy. A new model, the Mixed Effects Structural Equations (MESE) model which combines structural equations modeling and item response theory, is introduced to attend to measurement error bias when using several latent variables as predictors in generalized linear models. The paper investigates racial and gender disparities in STEM retention in higher education. Using the MESE model with 1997 National Longitudinal Survey of Youth data, I fin...
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作者：Winderbaum, Lyron J.; Koch, Inge; Gustafsson, Ove J. R.; Meding, Stephan; Hoffmann, Peter
作者单位：University of Adelaide
摘要：Imaging mass spectrometry (IMS) has transformed proteomics by providing an avenue for collecting spatially distributed molecular data. Mass spectrometry data acquired with matrix assisted laser desorption ionization (MALDI) IMS consist of tens of thousands of spectra, measured at regular grid points across the surface of a tissue section. Unlike the more standard liquid chromatography mass spectrometry, MALDI-IMS preserves the spatial information inherent in the tissue. Motivated by the need t...
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作者：Guillot, Dominique; Rajaratnam, Bala; Emile-Geay, Julien
作者单位：Stanford University; University of Southern California; University of Southern California
摘要：Understanding centennial scale climate variability requires data sets that are accurate, long, continuous and of broad spatial coverage. Since instrumental measurements are generally only available after 1850, temperature fields must be reconstructed using paleoclimate archives, known as proxies. Various climate field reconstructions (CFR) methods have been proposed to relate past temperature to such proxy networks. In this work, we propose a new CFR method, called GraphEM, based on Gaussian M...
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作者：Wager, Stefan; Blocker, Alexander; Cardin, Niall
作者单位：Stanford University; Alphabet Inc.; Google Incorporated
摘要：Consider a classification problem where we do not have access to labels for individual training examples, but only have average labels over sub-populations. We give practical examples of this setup and show how such a classification task can usefully be analyzed as a weakly supervised clustering problem. We propose three approaches to solving the weakly supervised clustering problem, including a latent variables model that performs well in our experiments. We illustrate our methods on an analy...
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作者：Cahill, Niamh; Kemp, Andrew C.; Horton, Benjamin P.; Parnell, Andrew C.
作者单位：University College Dublin; Tufts University; Rutgers University System; Rutgers University New Brunswick; Rutgers University System; Rutgers University New Brunswick
摘要：We perform Bayesian inference on historical and late Holocene (last 2000 years) rates of sea-level change. The input data to our model are tide-gauge measurements and proxy reconstructions from cores of coastal sediment. These data are complicated by multiple sources of uncertainty, some of which arise as part of the data collection exercise. Notably, the proxy reconstructions include temporal uncertainty from dating of the sediment core using techniques such as radiocarbon. The model we propo...
