Results 31 to 40 of about 8,779,194 (300)
Linear Regression Based Real-Time Filtering
Advances in Electrical and Electronic Engineering, 2013 This paper introduces real time filtering method based on linear least squares fitted line. Method can be used in case that a filtered signal is linear. This constraint narrows a band of potential applications.
Misel Batmend, Daniela Perdukova
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An improved quantum-inspired algorithm for linear regression [PDF]
Quantum, 2022 We give a classical algorithm for linear regression analogous to the quantum matrix inversion algorithm [Harrow, Hassidim, and Lloyd, Physical Review Letters'09] for low-rank matrices [Wossnig, Zhao, and Prakash, Physical Review Letters'18], when the ...
András Gilyén, Zhao Song, Ewin Tang
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Building energy performance forecasting: A multiple linear regression approach
Applied Energy, 2019 Different ways to evaluate the building energy balance can be found in literature, including comprehensive techniques, statistical and machine-learning methods and hybrid approaches.
G. Ciulla, A. D’Amico
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On Optimal Interpolation In Linear Regression
CoRR, 2021 25 pages, 7 figures, to appear in NeurIPS ...
Oravkin, E, Rebeschini, P
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Transportes, 2020 Estimating the number of trips generated by a company is an essential part of the process of freight demand modelling. In this context, the current study examines freight trip generation to buildings under construction (BUC) using generalised linear ...
Leise Kelli de Oliveira +3 more
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Efficient Algorithms and Lower Bounds for Robust Linear Regression [PDF]
ACM-SIAM Symposium on Discrete Algorithms, 2018 We study the problem of high-dimensional linear regression in a robust model where an $\epsilon$-fraction of the samples can be adversarially corrupted. We focus on the fundamental setting where the covariates of the uncorrupted samples are drawn from a ...
Ilias Diakonikolas +2 more
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Distributed Online Linear Regressions
IEEE Transactions on Information Theory, 2021 We study online linear regression problems in a distributed setting, where the data is spread over a network. In each round, each network node proposes a linear predictor, with the objective of fitting the \emph{network-wide} data. It then updates its predictor for the next round according to the received local feedback and information received from ...
Deming Yuan +2 more
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Multiple Linear Regression versus Automatic Linear Modelling
Arquivo Brasileiro de Medicina Veterinária e ZootecniaIn this study, performances of Multiple Linear Regression and Automatic Linear Modelling are compared for different sample sizes and number of predictors. A comprehensive Monte Carlo simulation study was carried out for this purpose.
S. Genç, M. Mendeş
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Consequences of ignoring clustering in linear regression
BMC Medical Research Methodology, 2021 Background Clustering of observations is a common phenomenon in epidemiological and clinical research. Previous studies have highlighted the importance of using multilevel analysis to account for such clustering, but in practice, methods ignoring ...
Georgia Ntani +3 more
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CoRR, 2015 We consider the problem of fitting a linear model to data held by individuals who are concerned about their privacy. Incentivizing most players to truthfully report their data to the analyst constrains our design to mechanisms that provide a privacy guarantee to the participants; we use differential privacy to model individuals' privacy losses.
Cummings, Rachel +2 more
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