Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/66297
Authors: 
Maca, J. D.
Carroll, Raymond J.
Ruppert, David
Year of Publication: 
1997
Series/Report no.: 
Discussion Papers, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes 1997,11
Abstract: 
In many regression applications both the independent and dependent variables are measured with error. When this happens, conventional parametric and nonparametric regression techniques are no longer valid. We consider two different nonparametric techniques, regression splines and kernel estimation, of which both can be used in the presence of measurement error. Within the kernel regression context, we derive the limit distribution of the SIMEX estimate. With the regression spline technique, two different methods of estimations are used. The first method is the SIMEX algorithm which attempts to estimate the bias, and remove it. The second method is a structural approach, where one hypothesizes a distribution for the independent variable which depends on estimable parameters. A series of examples and simulations illustrate the methods.
Subjects: 
Bootstrap
Measurement Error
Local Polynomial Regression
SIMEX
Asymptotic theory
Estimating Equations
Nonlinear Regression
Bandwidth Selection
Regression Splines
Sandwich Estimation
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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