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On the Weak Convergence and Central Limit Theorem of Blurring and Nonblurring Processes with Application to Robust Location Estimation

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Ting-Li Chen, Hironori Fujisawa, Su-Yun Huang, Chii-Ruey Hwang

Statistics Theory · Submitted 3 Dec 2014

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

The authors' main claim Simulation studies show that the location estimation based on the convergence point of blurring process is more robust and often more efficient than that of nonblurring process.

how it is stated · asserted 0.99 read the record →

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arXiv ID1412.1411 (version 3)
Pith IDZZOVDCJV, Pith's permanent record of this paper and its review
CitationChii-Ruey Hwang, Hironori Fujisawa, Su-Yun Huang, Ting-Li Chen. "On the Weak Convergence and Central Limit Theorem of Blurring and Nonblurring Processes with Application to Robust Location Estimation." arXiv:1412.1411 (2014). https://pith.science/paper/ZZOVDCJV
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The compiled record

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Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,877 input tokens, $0.0001 reported.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.08 · human participants 0.03 · controlled comparison 0.51 · randomized assignment 0.02 · states quantitative result 0.07 · compares with prior results 0.83 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v3. Results and the main claim are underlined; .

This article studies the weak convergence and associated Central Limit Theorem for blurring and nonblurring processes. Then, they are applied to the estimation of location parameter. Simulation studies show that the location estimation based on the convergence point of blurring process is more robust and often more efficient than that of nonblurring process.

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 This article studies the weak convergence and associated Central Limit Theorem for blurring and nonblurring processes. contribution 1.00 0.82 0.06 asserted 0.84
s1 Then, they are applied to the estimation of location parameter. contribution 0.69 0.58 0.06 asserted 0.95
s2 Simulation studies show that the location estimation based on the convergence point of blurring process is more robust and often more efficient than that of nonblurring process. result 0.99 0.92 0.79 asserted 0.99

What kind of paper the classifier reports this is: theoretical result 0.97 · evidence from simulation or computation 0.90.

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not examined
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  • figures and tables
  • references and citing sentences
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  • P abstract
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  • 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

This article studies the weak convergence and associated Central Limit Theorem for blurring and nonblurring processes. Then, they are applied to the estimation of location parameter. Simulation studies show that the location estimation based on the convergence point of blurring process is more robust and often more efficient than that of nonblurring process.

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