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Record W4410317522 · doi:10.1080/03610926.2025.2495347

Functional partially linear single-index model with beta distribution

2025· article· en· W4410317522 on OpenAlexfundno aff
Mengyu Zhang, Chao Huang, Fengchang Xie

Bibliographic record

VenueCommunication in Statistics- Theory and Methods · 2025
Typearticle
Languageen
FieldMathematics
TopicStatistical Methods and Inference
Canadian institutionsnot available
FundersDoD Alzheimer's Disease Neuroimaging InitiativeNational Institute of Biomedical Imaging and BioengineeringCanadian Institutes of Health ResearchGE HealthcareNational Institutes of HealthGenentechIXICONorthern California Institute for Research and EducationPfizerNovartis Pharmaceuticals CorporationRocheUniversity of Southern CaliforniaBiogenEli Lilly and CompanyBristol-Myers SquibbEisai IncorporatedTakeda Pharmaceutical CompanyAbbVieMerckNational Institute on AgingAlzheimer's AssociationU.S. Department of Defense
KeywordsBETA (programming language)Single-index modelIndex (typography)MathematicsBeta distributionStatisticsComputer scienceApplied mathematics

Abstract

fetched live from OpenAlex

.In this work, for depicting the relationship between a bounded scalar response and predictors including both scalars and functions, we develop a functional partially linear single-index model based on beta distribution, which can capture the bounded feature of the response well and also provides more flexibility. Approximating the unknown functions based on B-spline simultaneously, the parametric estimates of the proposed model are obtained. Under some conditions, the asymptotic properties of the resulting estimators, including the consistency and asymptotic normality, are derived. In addition, the finite sample performance of our method is validated through some simulation studies. Meantime, real data from the Alzheimer’s disease neuroimaging initiative (ADNI) study is investigated, and the results show that our proposed model outperforms competitors.

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 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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.118
Threshold uncertainty score0.557

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.116
GPT teacher head0.447
Teacher spread0.331 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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