Last updated: 2018-05-12

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• ✔ R Markdown file: up-to-date

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• ✔ Repository version: 5b37742

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Expand here to see past versions:

File	Version	Author	Date	Message
Rmd	cc0ab83	Lei Sun	2018-05-11	update
html	0f36d99	LSun	2017-12-21	Build site.
html	853a484	LSun	2017-11-07	Build site.
Rmd	2a9b0b7	LSun	2017-03-30	weights
html	2a9b0b7	LSun	2017-03-30	weights
html	d575294	LSun	2017-01-31	Build site.
Rmd	23a60a2	LSun	2017-01-29	readme
html	23a60a2	LSun	2017-01-29	readme
Rmd	1d187aa	LSun	2017-01-29	First Commit
html	1d187aa	LSun	2017-01-29	First Commit

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Building on Prof. Matthew Stephens’s insight that under correlation, the empirical distributions of the observations are different from their theoretical one. However, correlation affacts observations unequally. Moderate observations, defined as \(|\hat\beta / \hat s| \leq t\) with a pre-specified \(t\), are more prone to correlation and thus contain less information to control false discoveries than extreme ones, truncash makes partial use of moderate observations, combined with full use of extreme ones, to adaptively shrink the measurements with heteroskedastic noise.

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This reproducible R Markdown analysis was created with workflowr 1.0.1

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