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Record W2189594181

Supplementary Appendix to \Sequential Estimation of Structural Models with a Fixed Point Constraint"

2011· article· en· W2189594181 on OpenAlexaff
Hiroyuki Kasahara, Katsumi Shimotsu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMathematics
TopicRandom Matrices and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMathematicsCombinatoricsConstant (computer programming)Random variablePropositionFixed pointDiscrete mathematicsConstraint (computer-aided design)StatisticsMathematical analysisComputer sciencePhilosophyGeometry
DOInot available

Abstract

fetched live from OpenAlex

This supplementary appendix contains the following details omitted from the main paper due to space constraints: (A) numerical implementation of the sequential algorithm based on the RPM, (B) the sequential GMM estimator, (C) the convergence properties of the NPL algorithm for models with unobserved heterogeneity, (D) relative efficiency of the NPL, q-NPL, and MLE, and (E) the equivalence of the NPL estimator using Λ(P, θ) and the NPL estimator using Ψ(P, θ). A Numerical Implementation of the Sequential Algorithm based on the RPM in Section 4.2 Implementing the sequential algorithm based on the RPM in Section 4.2 requires evaluating (I − Π ( ˜ θj−1, ˜ Pj−1)∇P ′Ψ( ˜ θj−1, ˜ Pj−1)Π ( ˜ θj−1, ˜ Pj−1)) −1 as well as computing an orthonormal basis Z ( ˜ θj−1, ˜ Pj−1) from the eigenvectors of ∇P ′Ψ( ˜ θj−1, ˜ Pj−1) for j = 1,..., k. This is potentially costly when the analytical expression of ∇P ′Ψ(θ, P) is not available. In this section, we discuss how to reduce the computational cost of implementing the RPM algorithm by updating (I − Π ( ˜ θj−1, ˜ Pj−1)∇P ′Ψ( ˜ θj−1, ˜ Pj−1)Π ( ˜ θj−1, ˜ Pj−1)) −1 and Z ( ˜ θj−1, ˜ Pj−1) without explicitly computing ∇P ′Ψ(θ, P) in each iteration. Denote ˜ Πj−1 = Π ( ˜ θj−1, ˜ Pj−1),

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)0.002
metaresearch head score (Gemma)0.029
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient 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: none
Teacher disagreement score0.679
Threshold uncertainty score0.458

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.029
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.6790.207

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 head0.058
GPT teacher head0.305
Teacher spread0.247 · 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.

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".

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Citations0
Published2011
Admission routes1
Has abstractyes

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Same topicRandom Matrices and ApplicationsFrench-language works237,207