Results 1 to 10 of about 167,791 (166)
Brain kernel: A new spatial covariance function for fMRI data. [PDF]
Neuroimage, 2021 A key problem in functional magnetic resonance imaging (fMRI) is to estimate spatial activity patterns from noisy high-dimensional signals. Spatial smoothing provides one approach to regularizing such estimates. However, standard smoothing methods ignore
Wu A +6 more
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A Generic Approach to Covariance Function Estimation Using ARMA-Models
Mathematics, 2020 Covariance function modeling is an essential part of stochastic methodology. Many processes in geodetic applications have rather complex, often oscillating covariance functions, where it is difficult to find corresponding analytical functions for ...
Till Schubert +2 more
exaly +3 more sources
Introduction to Neutrosophic Stochastic Processes [PDF]
Neutrosophic Sets and Systems, 2023 In this article, the definition of literal neutrosophic stochastic processes is presented for the first time in the form 𝒩𝑡 = 𝜉𝑡 + 𝜂𝑡𝐼 ;𝐼 2 = 𝐼 where both {𝜉(𝑡),𝑡 ∈ 𝑇} and {𝜂(𝑡),𝑡 ∈ 𝑇} are classical real valued stochastic processes.
Mohamed Bisher Zeina, Yasin Karmouta
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FUNCTIONAL SEQUENTIAL TREATMENT ALLOCATION WITH COVARIATES [PDF]
Econometric Theory, 2023 We consider a sequential treatment problem with covariates. Given a realization of the covariate vector, instead of targeting the treatment with highest conditional expectation, the decision-maker targets the treatment which maximizes a general functional of the conditional potential outcome distribution, e.g., a conditional quantile, trimmed mean, or ...
Kock, Anders Bredahl +2 more
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Functional PCA With Covariate-Dependent Mean and Covariance Structure
Technometrics, 2022 28 pages, 3 ...
Fei Ding +3 more
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Covariate Model of Pixel Vector Intensities of Invasive H. sosnowskyi Plants
Journal of Imaging, 2021 This article describes an agricultural application of remote sensing methods. The idea is to aid in eradicating an invasive plant called Sosnowskyi borscht (H. sosnowskyi).
Ignas Daugela +4 more
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Dizhen xuebao, 2020 基于非均匀分布的陆地重力观测数据，重构局部重力场模型是区域重力资料处理与解释的重要环节。本文对比了多种局部重力场建模方法，并以EGM2008模型提供的自由空气重力异常模型重采样数据进行测试，综合比较了不同噪声条件下不同建模方法的实际效果。结果表明：在不同噪声水平下，优选出适合重力位场问题的协方差函数后，最小二乘配置法的建模效果优于其它方法。
Mingming Ruan, Shi Chen, Jiancheng Han
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Analysis of Dynamic Parameters of the System of Raster Formation and Control
Communications, 2020 The presented research work analyzes the sensing system, the main aim of which is a raster formation and controlling this process using the optical measuring equipment and high precision angle encoders. Optical measuring equipment are used for the raster
Antanas Fursenko +6 more
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Learning a Depth Covariance Function
2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023 We propose learning a depth covariance function with applications to geometric vision tasks. Given RGB images as input, the covariance function can be flexibly used to define priors over depth functions, predictive distributions given observations, and methods for active point selection. We leverage these techniques for a selection of downstream tasks:
Eric Dexheimer, Andrew J. Davison
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Geography, Environment, Sustainability, 2022 Bandwidth plays a crucial role in the Geographically Weighted Regression modelas it affects the model’s ability to describe spatial dependencies. If the bandwidth is too large, the model will be similar to a normal regression model.
Winsy Weku +3 more
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