Results 281 to 290 of about 8,978,880 (312)

Identification of a class of dynamic errors‐in‐variables models

International Journal of Adaptive Control and Signal Processing, 1992
AbstractDynamic errors‐in‐variables (EV) models are a new type of linear system models and have found extensive practical applications. One common and important concern with EV models is how to remove noise‐induced bias in parameter estimators. In this paper some significant extensions to the newly established bias‐eliminated least‐squares (BELS ...
Zheng, Wei-Xing, Feng, Chun-Bo
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Identification of Scalar Errors-in-Variables Models with Dynamics

IFAC Proceedings Volumes, 1985
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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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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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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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.
Lydia Ng, Victor Solo
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A Semi‐parametric Regression Model with Errors in Variables

Scandinavian Journal of Statistics, 2003
Abstract.  In this paper, we consider a partial linear regression model with measurement errors in possibly all the variables. We use a method of moments and deconvolution to construct a new class of parametric estimators together with a non‐parametric kernel estimator.
Zhu, L, Cui, H
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Estimation of errors-in-variables models

Proceedings of the 27th IEEE Conference on Decision and Control, 2003
The so-called errors-in-variables models pose serious problems to traditional statistical estimation because the Gaussian likelihood function, defined by the natural quadratic error measure, has a saddle point rather than a maximum. A discussion is presented of the estimation of such models, including the number of linear relations in them, based on ...
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Freeform surface topography model for ultraprecision turning under the influence of various errors

Journal of Manufacturing Processes, 2021
Jianping Xuan, Wenhao Du, Qi Xia
exaly  

A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts

International Journal of Mechanical Sciences, 2021
Yuwen Sun, Fangyu Peng, Shengqiang Zhao   +2 more
exaly  

Effective estimation of nonlinear errors-in-variables models

Communications in Statistics - Simulation and Computation, 2023
Zhensheng Huang, Shuyu Meng, Ziyi Ye
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