A procedure for robust fitting in nonlinear regression

2009 ◽  
Vol 53 (12) ◽  
pp. 4500-4507 ◽  
Author(s):  
Douglas M. Hawkins ◽  
Dost Muhammad Khan
Keyword(s):  
Nonlinear Regression ◽  
Robust Fitting ◽  
Fitting In

Computerized curve fitting in the analysis of hydrogen gas clearance curves

1989 ◽  
Vol 257 (4) ◽  
pp. G668-G675 ◽  
Author(s):  
E. H. Livingston ◽  
T. Reedy ◽  
F. W. Leung ◽  
P. H. Guth
Keyword(s):  
Blood Flow ◽  
Linear Regression ◽  
Nonlinear Regression ◽  
Curve Fitting ◽  
Regression Line ◽  
Hydrogen Gas ◽  
Base Line ◽  
Hydrogen Gas Clearance ◽  
Experimental Noise ◽  
Fitting In

Hydrogen gas clearance curves obtained from the rat gastric corpus were digitized into a computer and then analyzed by three methods: 1) linear regression of log-transformed data, 2) direct curve fitting with a modified Gauss-Newton nonlinear regression algorithm, and 3) Zierler's height-over-area algorithm. For linear regression of log-transformed data, if the initial base-line estimate was inaccurate or normal amounts of experimental noise were present, the log-transformed data was skewed, leading to deviation of the regression line and incorrect estimation of blood flow. By utilization of the direct-fit routine, the initial estimate of the parameters or experimental noise had little influence on the blood flow determination because of iterative improvement of the parameters. In a study of isoproterenol-stimulated gastric blood flow, Zierler's algorithm underestimated the blood flow estimate. We conclude that analysis of hydrogen gas clearance curves by linear regression of log-transformed data or by Zierler's algorithm may potentially introduce errors in blood flow estimates that may be avoided by analysis with a direct-fitting, nonlinear regression algorithm.

scholarly journals Robust Fitting in Computer Vision: Easy or Hard?

2019 ◽  
Vol 128 (3) ◽  
pp. 575-587 ◽  
Author(s):  
Tat-Jun Chin ◽  
Zhipeng Cai ◽  
Frank Neumann
Keyword(s):  
Computer Vision ◽  
Robust Fitting ◽  
Fitting In

scholarly journals Robust Fitting in Computer Vision: Easy or Hard?

2018 ◽  
pp. 715-730 ◽  
Author(s):  
Tat-Jun Chin ◽  
Zhipeng Cai ◽  
Frank Neumann
Keyword(s):  
Computer Vision ◽  
Robust Fitting ◽  
Fitting In

Background Fitting in the Low-Loss Region of Electron Energy Loss Spectra

1990 ◽  
Vol 48 (2) ◽  
pp. 56-57
Author(s):  
C P Scott ◽  
A J Craven ◽  
C J Gilmore ◽  
A W Bowen
Keyword(s):  
Energy Loss ◽  
Multiple Scattering ◽  
Simple Form ◽  
Single Scattering ◽  
Low Loss ◽  
Characteristic Energy ◽  
Normal Method ◽  
Fitting In ◽  
Electron Energy Loss ◽  
Electron Energy Loss Spectra

The normal method of background subtraction in quantitative EELS analysis involves fitting an expression of the form I=AE-r to an energy window preceding the edge of interest; E is energy loss, A and r are fitting parameters. The calculated fit is then extrapolated under the edge, allowing the required signal to be extracted. In the case where the characteristic energy loss is small (E < 100eV), the background does not approximate to this simple form. One cause of this is multiple scattering. Even if the effects of multiple scattering are removed by deconvolution, it is not clear that the background from the recovered single scattering distribution follows this simple form, and, in any case, deconvolution can introduce artefacts.The above difficulties are particularly severe in the case of Al-Li alloys, where the Li K edge at ~52eV overlaps the Al L2,3 edge at ~72eV, and sharp plasmon peaks occur at intervals of ~15eV in the low loss region. An alternative background fitting technique, based on the work of Zanchi et al, has been tested on spectra taken from pure Al films, with a view to extending the analysis to Al-Li alloys.

Nonlinear Regression

2011 ◽  
Author(s):  
Stephen J. Guastello
Keyword(s):  
Nonlinear Regression

Ortho-k lenses fitting in patients with “non-typical” corneas

The Eye ◽  
2020 ◽  
Vol 22 (129) ◽  
pp. 22-29
Author(s):  
Svetlana Kravchuk ◽  
Olga Zhabina
Keyword(s):  
Correction Method ◽  
Diameter Ratio ◽  
Corneal Curvature ◽  
Individual Approach ◽  
Fitting In ◽  
Corneal Diameter ◽  
Clinical Cases

We described two clinical cases of ortho-k lenses fitting in patients with “non-typical” corneal curvature/diameter ratio. The main goal was to acknowledge effective and safe use of this myopia correction method in patients with corneal diameter greater than 11 mm. Individual approach to each patient is the key to a successful and safe ortho-k lenses fitting.

Response fitting in low-cost radiation sensors

2010 ◽  
Vol 1 (08) ◽  
pp. 920-925
Author(s):  
A. Gómez Moreno ◽  
P.J. Casanova Peláez ◽  
F.A. Díaz Garrido ◽  
J.M. Palomar Carnicero ◽  
R. López García ◽  
...  
Keyword(s):  
Low Cost ◽  
Fitting In ◽  
Radiation Sensors

Biracial Stereotypes Explored

2019 ◽  
Author(s):  
Allison Skinner ◽  
Sylvia Perry ◽  
Sarah Gaither
Keyword(s):  
Social Groups ◽  
Parent Group ◽  
Lower Status ◽  
Parent Groups ◽  
Fitting In ◽  
The U.S

Stereotypes often guide our perceptions of members of social groups. However, research has yet to document what stereotypes may exist for the fastest growing youth demographic in the U.S.—biracial individuals. Across seven studies (N = 1,104) we investigate what stereotypes are attributed to various biracial groups, whether biracial individuals are stereotyped as more similar to their lower status monoracial parent group (trait hypodescent), and whether contact moderates these stereotypes. Results provide evidence of some universal biracial stereotypes that are applied to all biracial groups: attractive and not fitting in or belonging. We also find that all biracial groups are attributed a number of unique stereotypes (i.e., which are not associated with their monoracial parent groups). However, across all studies, we find little evidence of trait hypodescent and no evidence that the tendency to engage in trait hypodescent varies as a function of contact.

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