Evidence map›Paper›PMID 20151306›Full record

ArticleJournal of mathematical biology2011

Composite likelihood-based inferences on genetic data from dependent loci.

Arindam RoyChoudhury

Abstract read
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In one paragraph

Article in Journal of mathematical biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
0.8field-weighted citation impact, top 25% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 16 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Arindam RoyChoudhuryDepartment of Biostatistics, Columbia University, New York, NY 10032, USA. ar2946@columbia.edu
Columbia University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The structure of dependence between neighboring genetic loci is intractable under some models that treat each locus as a single data-point. Composite likelihood-based methods present a simple approach under such models by treating the data as if they are independent. A maximum composite likelihood estimator (MCLE) is not easy to find numerically, as in most cases we do not have a way of knowing if a maximum is global. We study the local maxima of the composite likelihood (ECLE, the efficient composite likelihood estimators), which is straightforward to compute. We establish desirable properties of the ECLE and provide an estimator of the variance of MCLE and ECLE. We also modify two proper likelihood-based tests to be used with composite likelihood. We modify our methods to make them applicable to datasets where some loci are excluded.

Indexed as

Likelihood FunctionsModels, GeneticQuantitative Trait, HeritableChildComputer SimulationGenetics, PopulationHumans

Identifiers

PMID20151306
OpenAlexW1985955911

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.