Results 301 to 310 of about 1,735,832 (325)

Error-in-variables models in calibration

Metrologia, 2017
In many calibration operations, the stimuli applied to the measuring system or instrument under test are derived from measurement standards whose values may be considered to be perfectly known. In that case, it is assumed that calibration uncertainty arises solely from inexact measurement of the responses, from imperfect control of the calibration ...
I Lira, D Grientschnig
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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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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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Bayesian Analysis of Errors-in-Variables Regression Models

Biometrics, 1995
Summary: Use of errors-in-variables models is appropriate in many practical experimental problems. However, inference based on such models is by no means straightforward. In previous analyses, simplifying assumptions have been made in order to ease this intractability, but assumptions of this nature are unfortunate and restrictive. We analyse errors-in-
Dellaportas, Petros, Stephens, David A.
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Semiparametric errors-in-variables models A Bayesian approach

Journal of Statistical Planning and Inference, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Mallick, Bani K., Gelfand, Alan E.
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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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Prediction in Some Poisson Errors in Variables Models

Scandinavian Journal of Statistics, 1997
Predictive distributions are developed and illustrated for prediction in some Poisson errors in variables models. Two different situations in which multiplicative treatment effects are appropriate are considered within the context of predicting counts of road accidents. Hierarchical prior structures are investigated, and numerical integration and Gibbs
Dunsmore, Ian R., Robson, David J.
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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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Linear dynamic errors-in-variables models

Journal of Applied Probability, 1986
Linear dynamical systems where both inputs and outputs are contaminated by errors are considered. A characterization of the sets of all observationally equivalent transfer functions is given, the role of the causality assumption is investigated and conditions for identifiability in the case of Gaussian as well as non-Gaussian observations are derived.
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Errors-in-variables modeling in optical flow estimation

IEEE Transactions on Image Processing, 2001
Gradient-based optical flow estimation methods typically do not take into account errors in the spatial derivative estimates. The presence of these errors causes an errors-in-variables (EIV) problem. Moreover, the use of finite difference methods to calculate these derivatives ensures that the errors are strongly correlated between pixels.
Ng, Lydia, Solo, Victor
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