A False Discovery Rate Approach to Optimal Volatility Forecasting Model Selection

2020 ◽  
Author(s):  
Arman Hassanniakalager ◽  
Paul L Baker ◽  
Emmanouil Platanakis
Keyword(s):  
Model Selection ◽  
False Discovery Rate ◽  
Forecasting Model ◽  
Volatility Forecasting ◽  
False Discovery ◽  
False Discovery Rate Approach

scholarly journals A practical false discovery rate approach to identifying patterns of differential expression in microarray data

2005 ◽  
Vol 21 (11) ◽  
pp. 2684-2690 ◽  
Author(s):  
G. R. Grant ◽  
J. Liu ◽  
C. J. Stoeckert
Keyword(s):  
False Discovery Rate ◽  
Differential Expression ◽  
Microarray Data ◽  
False Discovery ◽  
False Discovery Rate Approach

scholarly journals Selective inference in complex research

2009 ◽  
Vol 367 (1906) ◽  
pp. 4255-4271 ◽  
Author(s):  
Yoav Benjamini ◽  
Ruth Heller ◽  
Daniel Yekutieli
Keyword(s):  
Type 2 Diabetes ◽  
False Discovery Rate ◽  
Confidence Intervals ◽  
False Discovery ◽  
False Discovery Rate Approach ◽  
Selective Inference

We explain the problem of selective inference in complex research using a recently published study: a replicability study of the associations in order to reveal and establish risk loci for type 2 diabetes. The false discovery rate approach to such problems will be reviewed, and we further address two problems: (i) setting confidence intervals on the size of the risk at the selected locations and (ii) selecting the replicable results.

scholarly journals Gene Prioritization for Imaging Genetics Studies Using Gene Ontology and a Stratified False Discovery Rate Approach

2016 ◽  
Vol 10 ◽  
Author(s):  
Sejal Patel ◽  
Min Tae M. Park ◽  
M. Mallar Chakravarty ◽  
Jo Knight
Keyword(s):  
Gene Ontology ◽  
False Discovery Rate ◽  
Gene Prioritization ◽  
Imaging Genetics ◽  
False Discovery ◽  
False Discovery Rate Approach

scholarly journals Variance model selection with application to joint analysis of multiple microarray datasets under false discovery rate control

2010 ◽  
Vol 3 (4) ◽  
pp. 477-491 ◽  
Author(s):  
Nicola Bacciu ◽  
Jack C. M. Dekkers ◽  
Dan Nettleton ◽  
Long Qu
Keyword(s):  
Model Selection ◽  
False Discovery Rate ◽  
Rate Control ◽  
Joint Analysis ◽  
Variance Model ◽  
False Discovery Rate Control ◽  
False Discovery ◽  
Microarray Datasets

scholarly journals False Discovery Rate Approach to Unsupervised Image Change Detection

2016 ◽  
Vol 25 (10) ◽  
pp. 4704-4718 ◽  
Author(s):  
Vladimir A. Krylov ◽  
Gabriele Moser ◽  
Sebastiano B. Serpico ◽  
Josiane Zerubia
Keyword(s):  
Change Detection ◽  
False Discovery Rate ◽  
False Discovery ◽  
Image Change Detection ◽  
False Discovery Rate Approach

scholarly journals False discovery rate approach to image change detection

2013 ◽  
Author(s):  
Vladimir A. Krylov ◽  
Gabriele Moser ◽  
Sebastiano B. Serpico ◽  
Josiane Zerubia
Keyword(s):  
Change Detection ◽  
False Discovery Rate ◽  
False Discovery ◽  
Image Change Detection ◽  
False Discovery Rate Approach

scholarly journals Using the False Discovery Rate Approach in the Genetic Dissection of Complex Traits: A Response to Weller et al.

Genetics ◽  
2000 ◽  
Vol 154 (4) ◽  
pp. 1917-1918 ◽  
Author(s):  
Dmitri V Zaykin ◽  
S Stanley Young ◽  
Peter H Westfall
Keyword(s):  
False Discovery Rate ◽  
Complex Traits ◽  
Genetic Dissection ◽  
False Discovery ◽  
False Discovery Rate Approach

scholarly journals Discovery of shared genomic loci using the conditional false discovery rate approach

2019 ◽  
Vol 139 (1) ◽  
pp. 85-94 ◽  
Author(s):  
Olav B. Smeland ◽  
Oleksandr Frei ◽  
Alexey Shadrin ◽  
Kevin O’Connell ◽  
Chun-Chieh Fan ◽  
...  
Keyword(s):  
False Discovery Rate ◽  
False Discovery ◽  
False Discovery Rate Approach

Linear Mixed Model Selection for False Discovery Rate Control in Microarray Data Analysis

Biometrics ◽  
2009 ◽  
Vol 66 (2) ◽  
pp. 621-629 ◽  
Author(s):  
Cumhur Yusuf Demirkale ◽  
Dan Nettleton ◽  
Tapabrata Maiti
Keyword(s):  
Data Analysis ◽  
Model Selection ◽  
False Discovery Rate ◽  
Microarray Data ◽  
Rate Control ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Microarray Data Analysis ◽  
False Discovery ◽  
Selection For
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