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Use of copula functions in error assessment due to deviation from dependence assumption

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Subarna Bhattacharjee, Aninda Kumar Nanda, Subhashree Patra

Statistics Theory · Submitted 27 Mar 2025

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

The authors' main claim This technique generalizes the existing work on error assessment for many wide class of distributions.

how it is stated · asserted 1.00 read the record →

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arXiv ID2503.21286 (version 1)
Pith ID4UCI644X, Pith's permanent record of this paper and its review
CitationAninda Kumar Nanda, Subarna Bhattacharjee, Subhashree Patra. "Use of copula functions in error assessment due to deviation from dependence assumption." arXiv:2503.21286 (2025). https://pith.science/paper/4UCI644X
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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.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,876 input tokens.

What the abstract itself states, as probabilities: claims first 0.05 · states limitation 0.08 · human participants 0.02 · controlled comparison 0.22 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.42 · mentions code or data availability 0.02.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In this paper, we analyze the relative errors in various reliability measures due to the tacit assumption that the components associated with a $n$-component series system or a parallel system are independently working where the components are dependent. We use Copula functions in said error analysis. This technique generalizes the existing work on error assessment for many wide class of distributions.

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, we analyze the relative errors in various reliability measures due to the tacit assumption that the components associated with a $n$-component series system or a parallel system are independently working where the components are dependent. contribution 0.87 0.65 0.13 asserted 0.95
s1 We use Copula functions in said error analysis. method 0.99 0.92 0.07 asserted 1.00
s2 This technique generalizes the existing work on error assessment for many wide class of distributions. contribution 0.80 0.64 0.21 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.73 · evidence from proof or derivation 0.95.

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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

In this paper, we analyze the relative errors in various reliability measures due to the tacit assumption that the components associated with a $n$-component series system or a parallel system are independently working where the components are dependent. We use Copula functions in said error analysis. This technique generalizes the existing work on error assessment for many wide class of distributions.

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