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
Local polynomial regressionErrors-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 ...
Errors-In-Variables regression and the problem of moments Al Sharadqah Ali*; Chernov Nikolai; Huang Qizhuo Brazilian Journal of Probability and Statistics (Brazilian Stat. Assoc.), 2013, 27(4): 401-415. 引用浏览(2) 1 被引 Constrained quadratic errors-in-variables fitting ...
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 ...
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 ...
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...
WaveletsThis 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-oscillating noises. Finally, we prove two strong consistency theorems for ...
TheGeneralIVRegressionModel CheckingInstrumentValidity Application WhereDoValidInstrumentsComeFrom? Threeimportantthreatstointernalvalidityare: omittedvariablebiasfromavariablethatiscorrelatedwithXbutisunobserved,socannotbeincludedintheregression; simultaneouscausalitybias(XcausesY,YcausesX); ...
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
Censored regressionErrors in variablesInstrumental variablesLimited dependent variablesMeasurement errorsMinimum distance estimationTobit modelTruncated outcomeSummary鈥 This paper deals with censored or truncated regression models where the explanatory variables are measured with additive errors. We propose a two...