Nonparametric kernel and regression spline estimation in the presence of measurement error

Arbeitspapier

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 When this happens, conventional parametric and nonparametric regression techniques are no longer valid.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.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Series: SFB 373 Discussion Paper ; No. 1997,11

Klassifikation und Themen

• Klassifikation: Wirtschaft
• Thema: Bootstrap ; Measurement Error ; Local Polynomial Regression ; SIMEX ; Asymptotic theory ; Estimating Equations ; Nonlinear Regression ; Bandwidth Selection ; Regression Splines ; Sandwich Estimation

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Maca, J. D. ; Carroll, Raymond J. ; Ruppert, David
• Ereignis: Veröffentlichung
• (wer): Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes
• (wo): Berlin
• (wann): 1997

Weitere Informationen

• Handle: http://hdl.handle.net/10419/66297
• URN: urn:nbn:de:kobv:11-10063710
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ