Results 211 to 220 of about 630,158 (264)
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LogitBoost with errors-in-variables
Computational Statistics & Data Analysis, 2008zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Joseph Sexton, Petter Laake
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Optimal errors-in-variables filtering
Automatica, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guidorzi R., Diversi R., Soverini U.
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Identifiability of errors in variables dynamic systems
Automatica, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Juan C. Agüero, Graham C. Goodwin
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Identification of nonlinear errors-in-variables models
Automatica, 2002The publication deals with a generalization of a classical eigenvalue-decomposition method first developed for errors-in-variables linear system identification. An identification algorithm is presented for nonlinear, but linear in parameters errors-in-variables models using nonlinear polynomial eigenvalue-eigenvector decompositions.
István Vajk, Jenö Hetthéssy
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Identifiability in dynamic errors-in-variables models
The 22nd IEEE Conference on Decision and Control, 1983Abstract. This paper is concerned with the identifiability of scalar linear dynamic errors‐in‐variables systems. The analysis is based on second moments only. The set of feasible systems corresponding to given second moments of the observations is described and conditions for identifiability are derived for the case of rational transfer functions.
Anderson, Brian D.O., Deistler, Manfred
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1987
This essay surveys the history and recent developments on economic models with errors in variables. These errors may arise from the use of substantive unobservables, such as permanent income, or from ordinary measurement problems in data collection and processing.
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This essay surveys the history and recent developments on economic models with errors in variables. These errors may arise from the use of substantive unobservables, such as permanent income, or from ordinary measurement problems in data collection and processing.
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Identification in the Linear Errors in Variables Model
Econometrica, 1983Consider the following multiple linear regression model with errors in variables: \(y_ j=\xi^ T\!_ j\beta +\epsilon_ j\), \(x_ j=\xi_ j+\nu_ j\), \(j=1,...,n\), where \(\xi_ j\), \(x_ j\), \(\nu_ j\), and \(\beta\) are k-vectors, \(y_ j\), \(\epsilon_ j\) are scalars. The \(\xi_ j\) are unobserved variables: instead the \(x_ j\) are observed.
Kapteyn, Arie, Wansbeek, Tom
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Boosted Regression Trees with Errors in Variables
Biometrics, 2007Summary In this article, we consider nonparametric regression when covariates are measured with error. Estimation is performed using boosted regression trees, with the sum of the trees forming the estimate of the conditional expectation of the response. Both binary and continuous response regression are investigated.
Sexton, Joseph, Laake, Petter
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Bootstrapping Errors-in-Variables Models
2000The bootstrap is a numerical technique, with solid theoretical foundations, to obtain statistical measures about the quality of an estimate by using only the available data. Performance assessment through bootstrap provides the same or better accuracy than the traditional error propagation approach, most often without requiring complex analytical ...
Bogdan Matei, Peter Meer
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