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Simultaneous Confidence Intervals and Regions

2017· other· en· W3157114989 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWiley StatsRef: Statistics Reference Online · 2017
Typeother
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsMcMaster University
Fundersnot available
KeywordsConfidence intervalConfidence distributionRobust confidence intervalsConfidence and prediction bandsCDF-based nonparametric confidence intervalStatisticsConfidence regionMathematicsCorrectnessInferenceRange (aeronautics)Computer scienceArtificial intelligenceAlgorithmEngineering

Abstract

fetched live from OpenAlex

Abstract Simultaneous confidence intervals constitute a confidence region for a vector of parameters, comprising individual intervals for the separate components, with a coverage confidence level of the simultaneous correctness of all the statements involved. The approach is delineated for multiple comparisons of treatment means with the methods of Bonferroni, Duncan, Scheffé, and Tukey, as specific examples. The applications of simultaneous confidence intervals to the inference of a therapeutic window or the reliability of composite systems necessitate the elucidation of multiple sets of simultaneous confidence intervals. Studies related to condensing infinite sets of simultaneous confidence intervals trigger a discussion on simultaneous confidence bands. Integrating the intrinsic information on the patterns or trends governing a set of simultaneous confidence intervals for the mean responses results in the methodology of range regression.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.142
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.322
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.142
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.486
GPT teacher head0.552
Teacher spread0.065 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it