Finite-sample comparison of robust estimators for nonlinear regression using Monte Carlo simulation: Part I. Univariate response models

Conceição, Eduardo L. T.; Portugal, António

doi:10.1016/j.compchemeng.2010.04.009

Cited by 3 publications

(4 citation statements)

References 32 publications

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“…In contrast, Huber's M-estimates do not show any clear advantage over conventional ML estimates. Contrary to our findings for univariate response models, 15 it is difficult to draw strong conclusions using this data alone. Clearly, more research is needed in this area to provide adequate guidelines.…”

Section: Discussioncontrasting

confidence: 99%

“…The same phenomenon is also observed in the simulation studies conducted for the case of univariate regression. 15 Considering that this also happens for the efficiency measure based on the mean squared error (not shown), it is not clear whether this is a genuine property of the estimators or simply an artifact of our data.…”

Section: Basics Of Decision Treesmentioning

confidence: 85%

“…In the univariate setting, Huber (see Huber and Ronchetti (p 198) 5 ) recommended comparing the estimates from the usual least-squares procedure with those of the M and one of the high BDP estimators, with the purpose of assessing discrepancies which merit closer scrutiny. Following this idea, a Monte Carlo simulation study was undertaken along the lines of that of Conceic -ão and Portugal 15 to gain some insight into the smallsample properties of the classical maximum likelihood (ML), M, and MLTS estimators in the context of kinetic modeling. More specifically, we investigate the effect of the fraction of outliers in the sample, outlying distance, and form of error distribution in terms of both bias and efficiency.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Two Robust Alternatives to the Box–Draper Determinant Criterion in Multiresponse Kinetic Parameter Estimation

Conceição

Portugal

2012

Ind. Eng. Chem. Res.

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Generalized least-squares and maximum likelihood approaches for parameter estimation in multivariate response models have been prevalent in the chemical kinetics literature to date. In contrast, robust alternatives have received considerably less attention. These methods safeguard against possible deviations from the assumptions, such as the presence of outliers or non-normality of the random errors. We compare, through Monte Carlo simulation, the performance of the classical Box–Draper determinant criterion (ML) to those of two robust estimators: the multivariate Huber’s M-estimator and the multivariate least-trimmed squares estimator (MLTS). Although the results are not entirely conclusive, overall, we find no compelling evidence for preferring any one of the two robust methods over the conventional ML estimates. At the same time, it was unexpected to find that ML is still reasonable under mild outlier contamination and mild deviations from normality. This notwithstanding, one loses nothing by comparing ML together with MLTS to cross-check each other as a safety measure.

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Section: Discussioncontrasting

confidence: 99%

Section: Basics Of Decision Treesmentioning

confidence: 85%

Section: Introductionmentioning

confidence: 99%

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Comparison of Two Robust Alternatives to the Box–Draper Determinant Criterion in Multiresponse Kinetic Parameter Estimation

Conceição

Portugal

2012

Ind. Eng. Chem. Res.

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“…Compared with traditional algebraic method, due to their reliance on repeated computation of random or pseudo-random numbers, Monte Carlo method can apply Normal distribution, Exponential distribution, Weibull distribution etc. to model phenomena with significant uncertainty in inputs when it is unfeasible or impossible to compute an exact result with a deterministic algo-rithm, and does not need to know parameter's distribution type and probability parameter [1]- [5]. In new product design, usually design parameter is a random variable that follows a probability distribution.…”

Section: Monte Carlo Methodsmentioning

confidence: 99%

A Monte Carlo Based Robustness Optimization Method in New Product Design Process: A Case Study

Che

Wang²,

Li³

2014

AJIBM

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Monte Carlo method can analyze, solve and optimize many mathematical or physical problems through generating a large number of statistical random samples to simulating stochastic events. It also can be used to remarkably improve design quality of new product. In new product design process, setting distribution characteristics of the design variables is vital to product quality and production robustness. Firstly, response surface model between output characteristics and design variables in new product design is proposed, and the distribution characteristics of design variables and response output are analyzed; then position error model of response output and standard value and allowed error maximum is presented; and then the differences of position error model and allowed error maximum are counted, and reliability ratio is built and calculated, and design robustness of the new product is increased by adjusting the precision value of random design variables in Monte Carlo experiments. Finally, a case is brought forward to verify the validity of the method.

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Performance evaluation of ground source heat pump using linear and nonlinear regressions and artificial neural networks

Liu

Wang

et al. 2020

Applied Thermal Engineering

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Finite-sample comparison of robust estimators for nonlinear regression using Monte Carlo simulation: Part I. Univariate response models

Cited by 3 publications

References 32 publications

Comparison of Two Robust Alternatives to the Box–Draper Determinant Criterion in Multiresponse Kinetic Parameter Estimation

Comparison of Two Robust Alternatives to the Box–Draper Determinant Criterion in Multiresponse Kinetic Parameter Estimation

A Monte Carlo Based Robustness Optimization Method in New Product Design Process: A Case Study

Performance evaluation of ground source heat pump using linear and nonlinear regressions and artificial neural networks

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