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Simultaneous Confidence Intervals

2014· other· en· W1494574517 on OpenAlexaff
Tuhao Chen, Fred M. Hoppe

Bibliographic record

VenueWiley StatsRef: Statistics Reference Online · 2014
Typeother
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsMcMaster University
Fundersnot available
KeywordsConfidence intervalBonferroni correctionCorrectnessRobust confidence intervalsStatisticsMultivariate statisticsMathematicsConfidence regionMultiple comparisons problemConfidence distributionEconometricsAlgorithm

Abstract

fetched live from OpenAlex

Abstract These provide a confidence region for a multivariate parameter, comprising individual intervals for the separate components of the parameter. There is a specified confidence level of the simultaneous correctness of all the statements. The approach is useful for multiple comparisons of treatment means. The methods of Bonferroni, Tukey, Scheffé and Duncan are described, with examples.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.069
metaresearch head score (Gemma)0.431
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.069
Threshold uncertainty score0.366

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0690.431
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0110.007
Science and technology studies0.0010.004
Scholarly communication0.0090.009
Open science0.0060.008
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0400.010

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 head0.399
GPT teacher head0.534
Teacher spread0.135 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2014
Admission routes1
Has abstractyes

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