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Record W2033792047 · doi:10.2307/3315926

Maximum likelihood estimation in generalized broken‐line regression

2004· article· en· W2033792047 on OpenAlexvenueno aff
Ryan Gill

Bibliographic record

VenueCanadian Journal of Statistics · 2004
Typearticle
Languageen
FieldMathematics
TopicAdvanced Statistical Methods and Models
Canadian institutionsnot available
Fundersnot available
KeywordsIdentifiabilityUniquenessExponential familyMaximum likelihoodEstimatorMathematicsApplied mathematicsEconometricsStatisticsRegressionMathematical analysis

Abstract

fetched live from OpenAlex

Abstract The author examines the existence, uniqueness, and identifiability of estimators produced by maximum likelihood for a model where the canonical parameter of an exponential family gradually begins to drift from its initial value at an unknown change point. He illustrates these properties with theoretical examples and applies his results to global warming data and failure data for emergency diesel generators.

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)0.011
metaresearch head score (Gemma)0.083
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.011
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.083
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.003
Scholarly communication0.0010.003
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.071
GPT teacher head0.373
Teacher spread0.302 · 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 designTheoretical or conceptual
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

Citations4
Published2004
Admission routes1
Has abstractyes

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