scholarly journals Optimal designs for testing the functional form of a regression via nonparametric estimation techniques

2001 ◽  
Vol 52 (2) ◽  
pp. 215-224 ◽  
Author(s):  
Stefanie Biedermann ◽  
Holger Dette
Keyword(s):  
Nonparametric Estimation ◽  
Functional Form ◽  
Optimal Designs ◽  
Estimation Techniques

scholarly journals Optimal designs for nonparametric estimation of zeros of regression functions

2012 ◽  
Vol 51 (1) ◽  
pp. 55-65
Author(s):  
Zdeněk Hlávka
Keyword(s):  
Confidence Interval ◽  
Nonparametric Estimation ◽  
Regression Function ◽  
Optimal Designs ◽  
Nonparametric Estimators ◽  
Regression Functions ◽  
Expected Width

ABSTRACT We investigate nonparametric estimators of zeros of a regression function and its derivatives and we derive the distribution of design points minimizing the expected width of a confidence interval and the expected variance of the proposed estimator.

Semiparametric IV Estimation with Parameter Dependent Instruments

1992 ◽  
Vol 8 (3) ◽  
pp. 403-406
Author(s):  
Paul Rilstone
Keyword(s):  
Nonparametric Estimation ◽  
Method Of Moments ◽  
Conditional Expectation ◽  
Asymptotic Efficiency ◽  
Functional Form ◽  
Moment Equations ◽  
Unknown Parameters ◽  
Iv Estimation ◽  
Sample Moment ◽  
Parameter Dependent

A well-known result in the method of moments literature is that the efficient instruments for the estimation of a model are functions of the conditional expectation of its gradient. Some recent studies have suggested the nonparametric estimation of these instruments when they are of unknown functional form. When these instruments in turn depend on the unknown parameters it has been suggested that these be replaced by preliminary consistent estimates. It is shown here that solving the sample moment equations simultaneously over the instruments and the residuals of the model will generally produce the same asymptotic efficiency and avoid the disadvantages inherent with the use of preliminary estimates.

Multi-modes for Detecting Experimental Measurement Error

2019 ◽  
Vol 28 (2) ◽  
pp. 263-283 ◽  
Author(s):  
Raymond Duch ◽  
Denise Laroze ◽  
Thomas Robinson ◽  
Pablo Beramendi
Keyword(s):  
Measurement Error ◽  
Nonparametric Estimation ◽  
Experimental Measurement ◽  
Treatment Effects ◽  
Heterogeneous Treatment Effects ◽  
Estimation Techniques ◽  
Diagnostic Strategies ◽  
Subject Pool ◽  
Experimental Protocols ◽  
Measurement Experiment

Experiments should be designed to facilitate the detection of experimental measurement error. To this end, we advocate the implementation of identical experimental protocols employing diverse experimental modes. We suggest iterative nonparametric estimation techniques for assessing the magnitude of heterogeneous treatment effects across these modes. And we propose two diagnostic strategies—measurement metrics embedded in experiments, and measurement experiments—that help assess whether any observed heterogeneity reflects experimental measurement error. To illustrate our argument, first we conduct and analyze results from four identical interactive experiments: in the lab; online with subjects from the CESS lab subject pool; online with an online subject pool; and online with MTurk workers. Second, we implement a measurement experiment in India with CESS Online subjects and MTurk workers.

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