Results 21 to 30 of about 116,008,209 (301)
IEEE Transactions on Information Theory, 2018 15 pages, 3 figures ...
Xiaoqing Fan +2 more
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On errors-in-variables estimation with unknown noise variance ratio
, 2006 We propose an estimation method for an errors-in-variables model with unknown input and output noise variances. The main assumption that allows identifiability of the model is clustering of the data into two clusters that are distinct in a certain ...
Van Huffel, S. +2 more
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Variable-Length Compression Allowing Errors [PDF]
IEEE Transactions on Information Theory, 2014 This paper studies the fundamental limits of the minimum average length of lossless and lossy variable-length compression, allowing a nonzero error probability $ε$, for lossless compression. We give non-asymptotic bounds on the minimum average length in terms of Erokhin's rate-distortion function and we use those bounds to obtain a Gaussian ...
Victoria Kostina +2 more
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Errors-in-Variables Models [PDF]
, 2000 Errors-in-variables (EIV) models axe regression models in which the regres-sors axe observed with errors. These models include the linear EIV models, the nonlinear EIV models, and the partially linear EIV models. Suppose that we want to investigate the relationship between the yield (Y) of corn and available nitrogen (X) in the soil.
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Asymptotic normality of total least squares estimator in a multivariate errors-in-variables model
Modern Stochastics: Theory and Applications, 2016 We consider a multivariate functional measurement error model $AX\approx B$. The errors in $[A,B]$ are uncorrelated, row-wise independent, and have equal (unknown) variances.
Alexander Kukush +1 more
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Identification of Fractional Models of an Induction Motor with Errors in Variables
Fractal and Fractional, 2023 The skin effect in modeling an induction motor can be described by fractional differential equations. The existing methods for identifying the parameters of an induction motor with a rotor skin effect suggest the presence of errors only in the output ...
Dmitriy Ivanov
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Solution for a time-series AR model based on robust TLS estimation
Geomatics, Natural Hazards & Risk, 2019 We discuss an algorithm for the autoregression (AR) model as a typical time-series model. By analyzing the structure of the AR model, we highlight the shortcomings of traditional algorithms for model parameter estimation and propose an approach to ...
Yeqing Tao, Qiaoning He, Yifei Yao
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EntropyDue to the high dimensional integration over latent variables, computing marginal likelihood and posterior distributions for the parameters of a general hierarchical model is a difficult task.
Subhash R. Lele +2 more
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On The Errors-In-Variables Model With Singular Dispersion Matrices
Journal of Geodetic Science, 2014 While the Errors-In-Variables (EIV) Model has been treated as a special case of the nonlinear Gauss- Helmert Model (GHM) for more than a century, it was only in 1980 that Golub and Van Loan showed how the Total Least-Squares (TLS) solution can be ...
Schaffrin B., Snow K., Neitzel F.
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Mathematics, 2020 In regression analysis, oftentimes a linear (or linearized) Gauss-Markov Model (GMM) is used to describe the relationship between certain unknown parameters and measurements taken to learn about them.
Burkhard Schaffrin
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