Results 21 to 30 of about 300,617 (266)
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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Synthese, 2012 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Douven, Igor, Kelp, Christoph
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The Bootstrapping Service [PDF]
26th IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW'06), 2006 We outline a lightweight architecture to support novel application scenarios for P2P systems. These scenarios include merging and splitting of large networks, or multiplexing relatively short-lived applications over a pool of shared resources. In such scenarios, the architecture needs to be quickly and efficiently (re)generated frequently, often from ...
Márk Jelasity +2 more
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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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The Stationary Bootstrap [PDF]
Journal of the American Statistical Association, 1994 Abstract This article introduces a resampling procedure called the stationary bootstrap as a means of calculating standard errors of estimators and constructing confidence regions for parameters based on weakly dependent stationary observations. Previously, a technique based on resampling blocks of consecutive observations was introduced to construct ...
Politis, Dimitris Nicolas +3 more
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Time Series Regression Modelling: Replication, Estimation and Aggregation through Maximum Entropy
Engineering Proceedings, 2023 In today’s world of large volumes of data, where the usual statistical estimation methods are commonly inefficient or, more often, impossible to use, aggregation methodologies have emerged as a solution for statistical inference.
Jorge Duarte, Maria Costa, Pedro Macedo
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Mathematics, 2021 Companies always seek ways to make their professional employees stay with them to reduce extra recruiting and training costs. Predicting whether a particular employee may leave or not will help the company to make preventive decisions.
Saeed Najafi-Zangeneh +3 more
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Model Error (or Ambiguity) and Its Estimation, with Particular Application to Loss Reserving
Risks, 2023 This paper is concerned with the estimation of forecast error, particularly in relation to insurance loss reserving. Forecast error is generally regarded as consisting of three components, namely parameter, process and model errors.
Greg Taylor, Gráinne McGuire
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Measuring Global Macroeconomic Uncertainty and Cross-Country Uncertainty Spillovers
Econometrics, 2022 We propose an approach for jointly measuring global macroeconomic uncertainty and bilateral spillovers of uncertainty between countries using a global vector autoregressive (GVAR) model.
Graziano Moramarco
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Random Forests for Regression as a Weighted Sum of
IEEE Access, 2019 In this paper, we tackle the problem of random forests for regression expressed as weighted sums of datapoints. We study the theoretical behavior of k-potential nearest neighbors (k-PNNs) under bagging and obtain an upper bound on the weights of a ...
Pablo Fernandez-Gonzalez +2 more
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