Results 11 to 20 of about 704,599 (275)
Frontiers in Public Health, 2021 This paper explores the necessity of expanding government expenditures on health (GEH) from the perspective of promoting residents' consumption (RC).
Ting-Yu Jiang
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A Robust Prediction Model for Species Distribution Using Bagging Ensembles with Deep Neural Networks
Remote Sensing, 2021 Species distribution models have been used for various purposes, such as conserving species, discovering potential habitats, and obtaining evolutionary insights by predicting species occurrence. Many statistical and machine-learning-based approaches have
Jehyeok Rew, Yongjang Cho, Eenjun Hwang
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SSRN Electronic Journal, 2011 We propose a methodology for constructing confidence regions with partially identified models of general form. The region is obtained by inverting a test of internal consistency of the econometric structure. We develop a dilation bootstrap methodology to deal with sampling uncertainty without reference to the hypothesized economic structure.
Galichon, Alfred, Henry, Marc
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A Residual Bootstrap for Conditional Value-at-Risk [PDF]
, 2020 This paper proposes a fixed-design residual bootstrap method for the two-step estimator of Francq and Zako\"ian (2015) associated with the conditional Value-at-Risk.
Beutner, Eric +2 more
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Synthese, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Douven, Igor, Kelp, Christoph
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SIAM Journal on Mathematics of Data Science, 2021 Probabilistic models of data sets often exhibit salient geometric structure. Such a phenomenon is summed up in the manifold distribution hypothesis, and can be exploited in probabilistic learning. Here we present normal-bundle bootstrap (NBB), a method that generates new data which preserve the geometric structure of a given data set.
Ruda Zhang, Roger Ghanem
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Using the Wild Bootstrap to Quantify Uncertainty in Mean Apparent Propagator MRI
Frontiers in Neuroinformatics, 2019 Purpose: Estimation of uncertainty of MAP-MRI metrics is an important topic, for several reasons. Bootstrap derived uncertainty, such as the standard deviation, provides valuable information, and can be incorporated in MAP-MRI studies to provide more ...
Xuan Gu +8 more
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A subsampled double bootstrap for massive data [PDF]
, 2015 The bootstrap is a popular and powerful method for assessing precision of estimators and inferential methods. However, for massive datasets which are increasingly prevalent, the bootstrap becomes prohibitively costly in computation and its feasibility is
Sengupta, Srijan +2 more
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Bootstrapping language acquisition [PDF]
Cognition, 2017 The semantic bootstrapping hypothesis proposes that children acquire their native language through exposure to sentences of the language paired with structured representations of their meaning, whose component substructures can be associated with words and syntactic structures used to express these concepts. The child's task is then to learn a language-
Abend, Omri +4 more
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Bootstrap for U-Statistics: A new approach [PDF]
, 2015 Bootstrap for nonlinear statistics like U-statistics of dependent data has been studied by several authors. This is typically done by producing a bootstrap version of the sample and plugging it into the statistic.
Sharipov, Olimjon Sh. +2 more
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