Results 251 to 260 of about 2,308,308 (297)

Bootstrapping Errors-in-Variables Models

2000
The 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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Error in Variables

2003
AbstractThis chapter analyses the standard regression model with errors in variables. It covers measurement error bias and unobserved heterogeneity bias, instrumental variable estimation with panel data. It presents estimates from Bover and Watson (2000) concerning economies of scale in a firm money demand equation.
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Identifiability in dynamic errors-in-variables models

The 22nd IEEE Conference on Decision and Control, 1983
Abstract. 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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Errors in all variables

AIP Conference Proceedings, 2005
We present a thorough derivation of the posterior for the straight line fit employing the hyper‐plane prior. For the example of the parabola we enlarge the scope to nonlinear problems, however simplify it to be solved resembling the straight line solution.
Preuss, R., Dose, V.
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Identification in the Linear Errors in Variables Model

Econometrica, 1983
Consider 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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Identification of nonlinear errors-in-variables models

Automatica, 2002
The 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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Linear errors-in-variables models

1984
In this paper we are concerned with the statistical analysis of systems, where both, inputs and outputs, are contaminated by errors. Models of this kind are called error-in-variables (EV) models. Let x t * . and y t * denote the “true” inputs and outputs respectively and let xt and yt denote the observed inputs and outputs, then the situation can be ...
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Errors in Variables and Articles

Evaluation Review, 1982
Quasi-experimental evaluations of manpower training may be biased when the mean value of preprogrammed earnings differs for participants and nonparticipants or when the two groups differ in the degree to which they deviate from the long-run trend of earnings. Both sources of bias are addressed in Director (1979).
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The Variance of Nonparametric Errors- in-Variables Estimates

IEEE Transactions on Instrumentation and Measurement, 2004
Frequency response functions (FRFs) measured by taking the ratio of the output to the input Fourier coefficients of the steady-state response of the system to a periodic excitation are considered. Under assumptions of additive Gaussian noise on both the inputs and outputs, the variance of such measurements is infinite.
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Error in Variable Conversion in Table

JAMA Surgery, 2023
Crisanto M, Torres, Elliott R, Haut, Joseph V, Sakran   +2 more
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