Empirical Bayes and semi-Bayes adjustments for a vast number of estimations. (Q51786591)

12 April 2018

title

Empirical Bayes and semi-Bayes adjustments for a vast number of estimations. (English)

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12 April 2018

author name string

Ulf Strömberg

series ordinal

1

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12 April 2018

publication date

8 October 2009

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12 April 2018

European Journal of Epidemiology

published in

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12 April 2018

volume

24

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12 April 2018

issue

12

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12 April 2018

page(s)

737-741

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12 April 2018

https://scigraph.springernature.com/pub.10.1007/s10654-009-9393-0

exact match

0 references

cites work

Empirical-Bayes and semi-Bayes approaches to occupational and environmental hazard surveillance

1 reference

Crossref

https://api.crossref.org/works/10.1007%2FS10654-009-9393-0

Using hierarchical modeling in genetic association studies with multiple markers: application to a case-control study of bladder cancer.

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Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes

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Resolving individuals contributing trace amounts of DNA to highly complex mixtures using high-density SNP genotyping microarrays

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Assessing the probability that a positive report is false: an approach for molecular epidemiology studies

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Principles of multilevel modelling

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Quantifying and correcting for the winner's curse in genetic association studies

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An alternative foundation for the planning and evaluation of linkage analysis. I. Decoupling "error probabilities" from "measures of evidence".

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Bias-reduced estimators and confidence intervals for odds ratios in genome-wide association studies

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Curses--winner's and otherwise--in genetic epidemiology

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Flexible design for following up positive findings

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Selecting "significant" differentially expressed genes from the combined perspective of the null and the alternative.

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Estimating odds ratios in genome scans: an approximate conditional likelihood approach

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Ranking of genome-wide association scan signals by different measures

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Overcoming the winner's curse: estimating penetrance parameters from case-control data

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Betting odds and genetic associations.

1 reference

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Selection of influential genetic markers among a large number of candidates based on effect estimation rather than hypothesis testing: an approach for genome-wide association studies

1 reference

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Reduction of selection bias in genomewide studies by resampling.

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Empirical Bayes adjustments for multiple results in hypothesis-generating or surveillance studies

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Why Most Discovered True Associations Are Inflated

1 reference

12 December 2020

10.1007/S10654-009-9393-0

Identifiers

DOI

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12 April 2018

PubMed ID

19813100

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12 April 2018