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Record W2950545869 · doi:10.22215/etd/2018-13359

Pairwise Estimating Equations for the Analysis of Linked Data

2018· dissertation· en· W2950545869 on OpenAlexaff
Abel Dasylva

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldDecision Sciences
TopicData Quality and Management
Canadian institutionsCarleton University
FundersSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsPairwise comparisonRecord linkageComputer scienceCovariateLinkage (software)Independence (probability theory)Data miningStatisticsEconometricsMathematicsMachine learningSociology

Abstract

fetched live from OpenAlex

In official statistics record linkage is an important activity, which consists in identifying records from the same individual in one or many files.It is used to combine data sources including admistrative, survey or big data sources.In practice, record linkage is subject to linkage errors when it relies on quasi-identifiers, such as names and demographic variables, which are nonunique and recorded with errors.Accounting for these errors is an important but challenging problem.In this work, two methods are described for the primary analysis of such data, i.e. an analysis by someone with unfettered access to all the related micro-data and project information.Both solu-B records are indexed over a subset B ⊂ {1, . . ., N }, where |B| is possibly different from |A|.We are interested in situations where the two files overlap significantly, i.e., where the ratio |A ∩ B| / min (|A|, |B|) is sufficiently large 1 .In the Cartesian product A * × B * , consider the pair (i, j) and define m ij and γ ij , the pair match status and comparison vector, respectively.Let M = [m ij ] 1≤i,j≤N denote the match matrix in A * × B * .In B * , let z j denote the observed responses from record j in B * , and define the vector z = z 1 . . .z N .As before, let X = x 1 . . .x N denote the matrix of all the covariates in register A * .Finally let y = y 1 . . .y N denote the actual responses, where y i is the actual response for record i in A * .As before, the finite population comprises of H IID blocks that each contain a variable but bounded number of IID individuals.Block h has size N h , where N h ≤ C for some constant C that does not depend on H, with N = N 1 + . . .+ N H .The block also corresponds to records indexed in the subsets A * h and B * h in the files A * and B * respectively, where |A * h | = |B * h | = N h .Let A h and B h denote the corresponding subsets in files A and B respectively, and let M h denote the match matrix in A * h × B * h ; the Cartesian product within the block. AssumptionsThe following assumptions are made that extend those of Section 3.3.A.1 The match matrix M h is a uniform random permutation independent of [x i ] i∈A * h .A.2 For i ∈ A * h , let j(i) denote the index of the corresponding record in B * h .The variables [y i ] i∈A * h , [I (j(i) ∈ B h )] i∈A * h , [I (i ∈ A h )] i∈A * h , M h , and {γ ij } (i,j)∈A * h ×B * h are conditionally mutually independent given the block size N h and the covariates [x i ] i∈A * h .1 When the overlap is small, statistical matching may be a better solution.where q ij is given by Eq. (4.1).Proof: Consider (i, j) ∈ A * h × B * h .As before, we have g (z j , x i ) =

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)0.052
metaresearch head score (Gemma)0.150
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.052
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0520.150
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0050.012
Science and technology studies0.0010.002
Scholarly communication0.0040.007
Open science0.0060.005
Research integrity0.0030.008
Insufficient payload (model declined to judge)0.0210.006

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 head0.545
GPT teacher head0.550
Teacher spread0.005 · 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 designSimulation or modeling
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
Published2018
Admission routes1
Has abstractyes

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