Pith. Sign in

RECORD

Appropriate use of parametric and nonparametric methods in estimating regression models with various shapes of errors

PaperPDF from arXiv

data files

Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
X Bluesky LinkedIn Reddit Hacker News
Mijeong Kim

Methodology · Submitted 5 Jan 2023

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

The authors' main claim In this regard, the proposed estimation methods utilize both parametric and nonparametric methods for errors appropriately.

how it is stated · asserted 1.00 read the record →

Comment (0) Read on arXiv ↗
Cite ▾
arXiv ID2301.02329 (version 2)
Pith IDMJYIWPCZ, Pith's permanent record of this paper and its review
CitationMijeong Kim. "Appropriate use of parametric and nonparametric methods in estimating regression models with various shapes of errors." arXiv:2301.02329 (2023). https://pith.science/paper/MJYIWPCZ
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,858 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.09 · human participants 0.03 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.12 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

In this paper, a practical estimation method for a regression model is proposed using semiparametric efficient score functions applicable to data with various shapes of errors. First, I derive semiparametric efficient score vectors for a homoscedastic regression model without any assumptions of errors. Next, the semiparametric efficient score function can be modified assuming a certain parametric distribution of errors according to the shape of the error distribution or by estimating the error distribution non-parametrically. Nonparametric methods for errors can be used to estimate the parameters of interest or to find an appropriate parametric error distribution. In this regard, the proposed estimation methods utilize both parametric and nonparametric methods for errors appropriately. Through numerical studies, the performance of the proposed estimation methods is demonstrated.

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 In this paper, a practical estimation method for a regression model is proposed using semiparametric efficient score functions applicable to data with various shapes of errors. contribution 1.00 0.91 0.28 asserted 1.00
s1 First, I derive semiparametric efficient score vectors for a homoscedastic regression model without any assumptions of errors. method 0.85 0.93 0.04 asserted 1.00
s2 Next, the semiparametric efficient score function can be modified assuming a certain parametric distribution of errors according to the shape of the error distribution or by estimating the error distribution non-parametrically. method 0.98 0.53 0.09 asserted 0.69
s3 Nonparametric methods for errors can be used to estimate the parameters of interest or to find an appropriate parametric error distribution. method 0.88 0.28 0.13 hedged possibility 0.68
s4 In this regard, the proposed estimation methods utilize both parametric and nonparametric methods for errors appropriately. method 0.54 0.80 0.28 asserted 1.00
s5 Through numerical studies, the performance of the proposed estimation methods is demonstrated. result 0.42 0.93 0.16 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.89.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

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

In this paper, a practical estimation method for a regression model is proposed using semiparametric efficient score functions applicable to data with various shapes of errors. First, I derive semiparametric efficient score vectors for a homoscedastic regression model without any assumptions of errors. Next, the semiparametric efficient score function can be modified assuming a certain parametric distribution of errors according to the shape of the error distribution or by estimating the error distribution non-parametrically. Nonparametric methods for errors can be used to estimate the parameters of interest or to find an appropriate parametric error distribution. In this regard, the proposed estimation methods utilize both parametric and nonparametric methods for errors appropriately. Through numerical studies, the performance of the proposed estimation methods is demonstrated.

Human review

How Pith checks papers → · Full audit of this paper →

Discussion

Know something about this paper? Leave a comment, or highlight any passage above to comment on it.