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Multivariate Analysis from a Statistical Point of View

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Kyle S. Cranmer

Data Analysis, Statistics and Probability · Submitted 22 Oct 2003

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

The authors' main claim We clarify what the goal of a multivariate algorithm should be for the search for a new particle and compare different approaches.

how it is stated · asserted 0.95 read the record →

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arXiv IDphysics/0310110 (version 2)
Pith IDLXK4E7YM, Pith's permanent record of this paper and its review
CitationKyle S. Cranmer. "Multivariate Analysis from a Statistical Point of View." arXiv:physics/0310110 (2003). https://pith.science/paper/LXK4E7YM
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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.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 2,866 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.09 · human participants 0.02 · controlled comparison 0.06 · randomized assignment 0.02 · states quantitative result 0.03 · compares with prior results 0.67 · mentions code or data availability 0.02.

The abstract, sentence by sentence

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

Multivariate Analysis is an increasingly common tool in experimental high energy physics; however, many of the common approaches were borrowed from other fields. We clarify what the goal of a multivariate algorithm should be for the search for a new particle and compare different approaches. We also translate the Neyman-Pearson theory into the language of statistical learning theory.

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 Multivariate Analysis is an increasingly common tool in experimental high energy physics; however, many of the common approaches were borrowed from other fields. background 0.99 0.05 0.04 asserted 1.00
s1 We clarify what the goal of a multivariate algorithm should be for the search for a new particle and compare different approaches. contribution 1.00 0.90 0.17 asserted 0.95
s2 We also translate the Neyman-Pearson theory into the language of statistical learning theory. contribution 0.83 0.87 0.11 asserted 0.97

What kind of paper the classifier reports this is: position or commentary 0.63 · evidence from argument 0.91.

What was examined, and what was not

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not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
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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

Multivariate Analysis is an increasingly common tool in experimental high energy physics; however, many of the common approaches were borrowed from other fields. We clarify what the goal of a multivariate algorithm should be for the search for a new particle and compare different approaches. We also translate the Neyman-Pearson theory into the language of statistical learning theory.

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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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