The GUM perspective on straight-line errors-in-variables regressionKaty Klauenberg aSteffen Martens bAlen Bonjakovi cMaurice G. Cox dAdriaan M.H. van der Veen eClemens Elster a
Errors-in-variablesVarying coefficient models inherit the simplicity and easy interpretation of classical linear models while enjoying the flexibility of nonparametric models. They are very useful in analyzing the relation between a response and a set of predictors. There has been no study, however, ...
eivreg — Errors-in-variables regression Description Remarks and examples Quick start Stored results Menu Methods and formulas Syntax References Options Also see Description eivreg fits errors-in-variables regression models when one or more of the independent variables are measured with error. To use ...
A Fast Algorithm for Errors-in-Variables Filtering Diversi Roberto* IEEE Transactions on Automatic Control, 2012, 57(5): 1303-1309. 引用浏览(3) Errors-In-Variables regression and the problem of moments Al Sharadqah Ali*; Chernov Nikolai; Huang Qizhuo ...
The multivariate errors-in-variables regression model is applicable when both dependent and independent variables in a multivariate regression are subject to measurement errors. In such a scenario it is long established that the traditional least squares approach to estimating the model parameters is ...
Errors-in-variablesFourier-oscillating noiseRegression functionStrong consistencyWaveletsThis paper studies the strong consistency of some estimators for an errors-in-variables regression model. We first provide an extension of Meister's theorem. Then, the same problem is dealt with under the Fourier-...
Summary Errors-in-variables regression is important in many areas of science and social science, e.g. in economics where it is often a feature of hedonic models, in environmental science where air quality indices are measured with error, in biology where the vegetative mass of plants is frequen...
Beta regression models provide an adequate approach for modeling continuous outcomes limited to the interval (0,1). This paper deals with an extension of beta regression models that allow for explanatory variables to be measured with error. The structura
errors-in-variablesbias(Xismeasuredwitherror). InstrumentalvariablesregressioncaneliminatebiaswhenE(u|X)≠0-usinganinstrumentalvariable,Z. OneRegressorandOneInstrument Loosely,IVregressionbreaksXintotwoparts:apartthatmightbecorrelatedwithu,andapartthatisnot.Byisolatingthepartthatisnotcorrelatedwithu,itispossibleto...
Monte Carlo simulations are employed to investigate the bias in linear regression parameters for cases in which both variables are subject to normally dist... A.H.,Kalantar,and,... - 《Talanta》 被引量: 24发表: 1995年 Bootstrap Standard Error and Confidence Intervals for the Difference Between...