Errors-in-variables models

found: Work cat.: Al-Sharadqah, A. Statistical analysis of curve fitting in errors in-variables models, 2011:abstr. (This dissertation is devoted to the problem of fitting geometric curves such as lines, circles, and ellipses to a set of experimental observations whose both coordinates are contaminated with noisy errors. This kind of regression is called Errors-in-Variables models (EIV), which is quite different and much more difficult to solve than the classical regression)

found: The annals of statistics, June 1984:p. 497 (In the errors-in-variables model, the true values of a set of variables satisfy exact relationships. Inference is based on the observed values which are the sums of the true values and errors of measurement)

found: International journal of adaptive control and signal processing, Sept. 2006:p. 337 (Although method of least-squares developed in the 18th century is still the most popular approach for determining the best fit to a given structure, this technique exhibits high sensitivity to errors in regressors. A generalized approach to modelling noise is to view all variables as contaminated by noise, called errors-in-variables (EIV) models. These models have broad application in time series modelling, image processing, signal processing, neural networks and system identification in the fields of engineering, econometrics, and statistics)

found: Biometrika, Mar. 1990:p. 127 (Statistical models whose independent variables are subject to measurement errors are often referred to as "errors-in-variables models")

found: Statistics in medicine, Nov. 10, 2008:p. 5217 (Errors-in-variables models (also known as measurement error models) extend the usual regression models toward a more realistic representation of the covariate. Observed values are interpreted as a [sic] error-prone proxy of the unobservable values of the covariate)

found: Journal de la Société française de statistique, via WWW, June 20, 2012:v. 153, no. 1 (2012), p. 52 (Statistical inference in classical regression models often assumes both response variable and possibly multidimensional predictors are fully observable. But, ... in numerous studies of practical importance predictors are often unobservable. Instead, one observes some surrogates for predictors. These models are often called errors-in-variables models or measurement errors models)

found: Google search, June 20, 2012("errors-in-variables models" -- 62,700 hits; "measurement error models" -- 48,700 hits; "measurement errors models" -- 20,400 hits)