A Profile-Based Approach to Parametric Sensitivity Analysis of Nonlinear Regression Models

Technometrics ◽

10.1198/00401700152672519 ◽

2001 ◽

Vol 43 (4) ◽

pp. 425-433 ◽

Author(s):

H Sulieman ◽

P. J McLellan ◽

D. W Bacon

Keyword(s):

Sensitivity Analysis ◽

Nonlinear Regression ◽

Regression Models ◽

Parametric Sensitivity ◽

Nonlinear Regression Models ◽

Parametric Sensitivity Analysis

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Statistical inference for nonlinear regression models

10.31274/rtd-180813-627 ◽

1971 ◽

Author(s):

A. Ronald Gallant

Keyword(s):

Statistical Inference ◽

Nonlinear Regression ◽

Regression Models ◽

Nonlinear Regression Models

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Statistical inference for nonlinear regression models

10.31274/rtd-180815-627 ◽

1971 ◽

Author(s):

A. Ronald Gallant

Keyword(s):

Statistical Inference ◽

Nonlinear Regression ◽

Regression Models ◽

Nonlinear Regression Models

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Synthesis of nonlinear regression models of magnetic nanofluid microlayer capacitance

Magnetohydrodynamics ◽

10.22364/mhd.55.4.11 ◽

2019 ◽

Vol 55 (4) ◽

pp. 491-498

Keyword(s):

Nonlinear Regression ◽

Regression Models ◽

Magnetic Nanofluid ◽

Nonlinear Regression Models

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Parametric sensitivity analysis for thermal runaway in semi-batch reactors: Application to cyclohexanone peroxide reactions

Journal of Loss Prevention in the Process Industries ◽

10.1016/j.jlp.2021.104436 ◽

2021 ◽

Vol 70 ◽

pp. 104436

Author(s):

Na Zang ◽

Xin-Ming Qian ◽

Chi-Min Shu ◽

Dejian Wu

Keyword(s):

Sensitivity Analysis ◽

Thermal Runaway ◽

Batch Reactors ◽

Parametric Sensitivity ◽

Parametric Sensitivity Analysis

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Parametric sensitivity analysis as an essential ingredient of spectroscopic ellipsometry data modeling: An application of the Morris screening method

Journal of Applied Physics ◽

10.1063/1.5126074 ◽

2019 ◽

Vol 126 (18) ◽

pp. 184901 ◽

Author(s):

D. V. Likhachev

Keyword(s):

Sensitivity Analysis ◽

Spectroscopic Ellipsometry ◽

Screening Method ◽

Data Modeling ◽

Essential Ingredient ◽

Parametric Sensitivity ◽

Parametric Sensitivity Analysis ◽

Ellipsometry Data ◽

Spectroscopic Ellipsometry Data

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Marginal Curvatures and Their Usefulness in the Analysis of Nonlinear Regression Models

Journal of the American Statistical Association ◽

10.1080/01621459.1987.10478507 ◽

1987 ◽

Vol 82 (399) ◽

pp. 844-850 ◽

Author(s):

G. P. Y. Clarke

Keyword(s):

Nonlinear Regression ◽

Regression Models ◽

Nonlinear Regression Models

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Third-order inference for autocorrelation in nonlinear regression models

Journal of Statistical Planning and Inference ◽

10.1016/j.jspi.2011.04.019 ◽

2011 ◽

Vol 141 (11) ◽

pp. 3413-3425 ◽

Author(s):

P.E. Nguimkeu ◽

M. Rekkas

Keyword(s):

Nonlinear Regression ◽

Regression Models ◽

Third Order ◽

Nonlinear Regression Models

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Estimating the Size of Web Apps Created Using the CakePHP Framework by Nonlinear Regression Models with Three Predictors

10.1109/csit52700.2021.9648680 ◽

2021 ◽

Author(s):

Sergiy Prykhodko ◽

Andrii Prykhodko ◽

Ivan Shutko

Keyword(s):

Nonlinear Regression ◽

Regression Models ◽

Nonlinear Regression Models

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Identification of Dynamic Metabolic Flux Balance Models Based on Parametric Sensitivity Analysis

Industrial & Engineering Chemistry Research ◽

10.1021/acs.iecr.6b03331 ◽

2017 ◽

Vol 56 (8) ◽

pp. 1911-1919 ◽

Author(s):

Ricardo Martinez Villegas ◽

Hector Budman ◽

Ali Elkamel

Keyword(s):

Sensitivity Analysis ◽

Metabolic Flux ◽

Parametric Sensitivity ◽

Flux Balance ◽

Parametric Sensitivity Analysis

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Nonlinear regression models for correlated count data

Environmetrics ◽

10.1002/env.3170030206 ◽

1992 ◽

Vol 3 (2) ◽

pp. 211-222 ◽

Author(s):

R. T. Burnett ◽

J. Shedden ◽

D. Krewski

Keyword(s):

Nonlinear Regression ◽

Regression Models ◽

Nonlinear Regression Models

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