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Robust estimation of heteroscedastic regression models: a brief overview and new proposals

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Conceição Amado, Ana M. Bianco, Graciela Boente, Isabel M. Rodrigues

Methodology · Submitted 6 Nov 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The studied estimators combine weighted $MM-$regression estimators, to control the impact of high leverage points, and a robust method to estimate the parameters of the variance function.

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arXiv ID2311.02822 (version 2)
Pith IDDZJV5P5E, Pith's permanent record of this paper and its review
CitationAna M. Bianco, Concei\c{c}\~ao Amado, Graciela Boente, Isabel M. Rodrigues. "Robust estimation of heteroscedastic regression models: a brief overview and new proposals." arXiv:2311.02822 (2023). https://pith.science/paper/DZJV5P5E
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,241 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.10 · human participants 0.04 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.03 · compares with prior results 0.10 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

We collect robust proposals given in the field of regression models with heteroscedastic errors. Our motivation stems from the fact that the practitioner frequently faces the confluence of two phenomena in the context of data analysis: non--linearity and heteroscedasticity. The impact of heteroscedasticity on the precision of the estimators is well--known, however the conjunction of these two phenomena makes handling outliers more difficult. An iterative procedure to estimate the parameters of a heteroscedastic non--linear model is considered. The studied estimators combine weighted $MM-$regression estimators, to control the impact of high leverage points, and a robust method to estimate the parameters of the variance function.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 We collect robust proposals given in the field of regression models with heteroscedastic errors. contribution 0.86 0.73 0.06 asserted 0.89
s1 Our motivation stems from the fact that the practitioner frequently faces the confluence of two phenomena in the context of data analysis: non--linearity and heteroscedasticity. background 0.99 0.12 0.03 asserted 0.98
s2 The impact of heteroscedasticity on the precision of the estimators is well--known, however the conjunction of these two phenomena makes handling outliers more difficult. background 0.97 0.05 0.06 asserted 1.00
s3 An iterative procedure to estimate the parameters of a heteroscedastic non--linear model is considered. method 0.85 0.55 0.06 asserted 0.94
s4 The studied estimators combine weighted $MM-$regression estimators, to control the impact of high leverage points, and a robust method to estimate the parameters of the variance function. method 0.87 0.61 0.17 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.58 · evidence from literature 0.92.

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  • C citations
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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

We collect robust proposals given in the field of regression models with heteroscedastic errors. Our motivation stems from the fact that the practitioner frequently faces the confluence of two phenomena in the context of data analysis: non--linearity and heteroscedasticity. The impact of heteroscedasticity on the precision of the estimators is well--known, however the conjunction of these two phenomena makes handling outliers more difficult. An iterative procedure to estimate the parameters of a heteroscedastic non--linear model is considered. The studied estimators combine weighted $MM-$regression estimators, to control the impact of high leverage points, and a robust method to estimate the parameters of the variance function.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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