Nonlinear regression checking via local polynomial smoothing with applications to thermogravimetric analysis

2009 ◽  
Vol 23 (6) ◽  
pp. 275-282 ◽  
Author(s):  
Ricardo Cao ◽  
Salvador Naya
Keyword(s):  
Thermogravimetric Analysis ◽  
Nonlinear Regression ◽  
Local Polynomial Smoothing ◽  
Local Polynomial ◽  
Polynomial Smoothing

scholarly journals Measuring the Discrepancy of a Parametric Model via Local Polynomial Smoothing

2012 ◽  
Vol 40 (3) ◽  
pp. 455-470
Author(s):  
ANOUAR EL GHOUCH ◽  
MARC G. GENTON ◽  
TAOUFIK BOUEZMARNI
Keyword(s):  
Parametric Model ◽  
Local Polynomial Smoothing ◽  
Local Polynomial ◽  
Polynomial Smoothing

Binary quantile regression with local polynomial smoothing

2015 ◽  
Vol 189 (1) ◽  
pp. 24-40 ◽  
Author(s):  
Songnian Chen ◽  
Hanghui Zhang
Keyword(s):  
Quantile Regression ◽  
Local Polynomial Smoothing ◽  
Local Polynomial ◽  
Polynomial Smoothing ◽  
Binary Quantile Regression

Local Polynomial Smoothing

2006 ◽  
Author(s):  
Burkhardt Seifert ◽  
Theo Gasser
Keyword(s):  
Local Polynomial Smoothing ◽  
Local Polynomial ◽  
Polynomial Smoothing

scholarly journals A three-step estimation procedure using local polynomial smoothing for inconsistently sampled longitudinal data

2016 ◽  
Vol 35 (20) ◽  
pp. 3613-3622 ◽  
Author(s):  
Lei Ye ◽  
Ada O. Youk ◽  
Susan M. Sereika ◽  
Stewart J. Anderson ◽  
Lora E. Burke
Keyword(s):  
Longitudinal Data ◽  
Estimation Procedure ◽  
Local Polynomial Smoothing ◽  
Local Polynomial ◽  
Polynomial Smoothing

PROPERTIES OF DOUBLY ROBUST ESTIMATORS WHEN NUISANCE FUNCTIONS ARE ESTIMATED NONPARAMETRICALLY

2018 ◽  
Vol 35 (05) ◽  
pp. 1048-1087 ◽  
Author(s):  
Christoph Rothe ◽  
Sergio Firpo
Keyword(s):  
Orthogonality Condition ◽  
Robust Estimators ◽  
Dimensional Parameter ◽  
Local Polynomial Smoothing ◽  
Local Polynomial ◽  
Finite Dimensional ◽  
Wide Range ◽  
Polynomial Smoothing ◽  
Doubly Robust

An estimator of a finite-dimensional parameter is said to be doubly robust (DR) if it imposes parametric specifications on two unknown nuisance functions, but only requires that one of these two specifications is correct in order for the estimator to be consistent for the object of interest. In this article, we study versions of such estimators that use local polynomial smoothing for estimating the nuisance functions. We show that such semiparametric two-step (STS) versions of DR estimators have favorable theoretical and practical properties relative to other commonly used STS estimators. We also show that these gains are not generated by the DR property alone. Instead, it needs to be combined with an orthogonality condition on the estimation residuals from the nonparametric first stage, which we show to be satisfied in a wide range of models.

Local Polynomial Smoothing

2004 ◽  
Author(s):  
Burkhardt Seifert ◽  
Theo Gasser
Keyword(s):  
Local Polynomial Smoothing ◽  
Local Polynomial ◽  
Polynomial Smoothing

Local Polynomial Smoothing

2014 ◽  
Author(s):  
Burkhardt Seifert ◽  
Theo Gasser
Keyword(s):  
Local Polynomial Smoothing ◽  
Local Polynomial ◽  
Polynomial Smoothing
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