Results 221 to 230 of about 629,085 (265)

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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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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Boosted Regression Trees with Errors in Variables

Biometrics, 2007
Summary 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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The Degenerate Bounded Errors-in-Variables Model

SIAM Journal on Matrix Analysis and Applications, 2001
The paper is devoted to a special case of the error-in-variable problem. It is viewed as total least squares with bounds on the uncertainty in the coefficient matrix. The chosen approach advantage is given as a motivation for further considerations. Corresponding proofs and algorithm synthesis are presented.
Shivkumar Chandrasekaran, Ming Gu 0002, Ali H. Sayed, Keith E. Schubert   +3 more
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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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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 the Individual Structural Equation

International Economic Review, 1983
The main intention of this paper is a practical (from the applied point of view) and simple method of dealing with the problem of measurement errors in simultaneous equation models. As a result, the paper provides the conditions under which certain simultaneous equations models can be identified and estimated on a recursive equation-by-equation basis ...
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Decomposition of Variables and Correlated Measurement Errors

International Economic Review, 1993
This paper examines the bias in the OLS estimators when the regressors have measurement errors correlated in a particular manner. When a variable is decomposed into two components but only one of them is observed with error, the induced measurement error in the other component is identical but has the opposite sign.
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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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A Fast Algorithm for Errors-in-Variables Filtering

IEEE Transactions on Automatic Control, 2012
This note concerns the optimal estimation of the input and output sequences of linear time-invariant errors-in-variables (EIV) processes. An efficient recursive filtering algorithm is proposed. It is an innovation-based approach that relies on the triangular decomposition of block Toeplitz matrices.
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