Results 171 to 180 of about 2,080 (215)

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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A Note on an 'Errors in Variables' Model

Journal of the American Statistical Association, 1966
Abstract We consider an errors in variables model in which the ‘true’ part of the determining variable is generated by a simple forecasting mechanism. It is shown that the Least Squares errors in variables bias can be interpreted in terms of the parameters of the forecasting mechanism; and that the ‘standard’ result for this bias may no longer hold in ...
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Estimation in the polynomial errors-in-variables model

Science China Mathematics, 2002
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Zhang, Sanguo, Chen, Xiru
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On errors-in-variables for binary regression models

Biometrika, 1984
The authors consider binary regression models when the predictors have errors. Assuming that nuisance parameters are independently and normally distributed, the conditional likelihood was derived. When the measurement error is large, the usual estimates are unreliable and in this situation, the authors examine the conditional maximum likelihood ...
Carroll, Raymond J., Spiegelman, Clifford H., Lan, K. K. Gordon, Bailey, Kent T., Abbott, Robert D.   +4 more
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Identification of dynamic errors-in-variables models

Automatica, 1996
From the conclusion: ``The problem of identifying a causal linear dynamic system excited by a stationary zero-mean noise with unknown rational spectrum is considered for the case when the input-output measurements are corrupted by additive and uncorrelated noises of unknown rational spectra.'' The authors show that under mild conditions, the model is ...
Paolo Castaldi, Umberto Soverini
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Identification of multivariable errors-in-variables models

1999 European Control Conference (ECC), 1999
The paper deals with a new identification approach, based on a prediction error method, for multivariable errors-in-variables models (EIV). Starting from the ARMAX decomposition of MIMO EIV processes and congruence conditions between noisy sequences and the constraints of EIV representations, the simultaneous estimate of the model parameters and of the
Paolo Castaldi, Roberto Diversi, Roberto Guidorzi, Umberto Soverini   +3 more
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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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Estimation of the Quadratic Errors in Variables Model

Biometrika, 1982
The authors have constructed an estimator of the coefficient vector \(\beta\) in the quadratic functional model with errors \((e_ t,u_ t)\) that are independent normal random variables with zero mean and known covariance matrix. The asymptotic properties of the estimator have been studied.
Wolter, Kirk M., Fuller, Wayne A.
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Errors in variables Models [PDF]

, 2014
the participation rate should increase with the player’s observed strength, and the ...
Philippe Février, Lionel Wilner
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Extending the Classical Normal Errors-in-Variables Model

Econometrica, 1980
IT IS WELL KNOWN that least-squares estimates of the coefficients of a regression equation are inconsistent if any of the regressors are measured with error. The nature of these inconsistencies has been examined by Aigner [1], Blomqvist [2], Chow [3], Levi [5], McCallum [6], and Wickens [10] for the case in which a single regressor is subject to ...
Garber, Steven, Klepper, Steven
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