Discover and read the best of Twitter Threads about #JSM2021

Most recents (6)

What a fantastic way to start a week!

Thank you to @sallycmorton and @FDuBoisBowman for visiting us in the COPSS @COPSSNews leadership academy yesterday!

A short 🧵 on how these meetings came about and few things I learned from these wise folks.

1/n
First, the zoom meeting (pictured above) took place because of the visionaries @BhramarBioStat and Huixia Judy Wang, who conceived the idea of the COPSS Leadership Academy and brought it to fruition!

2/n
The purpose of this award is to "recognize early career statistical scientists who show evidence of and potential for leadership and who will help shape and strengthen the field".

#Statistics #DataScience 3/n

community.amstat.org/copss/awards/l…
Read 17 tweets
#JSM2021 panel led by @minebocek on upskilling for a statistician -- how to learn??
@minebocek #JSM2021 @hglanz no shortage of stuff to learn. First identify what you don't know -- that comes from modern media (blogs, twitter, podcasts; groups, communities -- @RLadiesGlobal or local chapters; professional organizations -- @amstatnews ).
@minebocek @hglanz @RLadiesGlobal @AmstatNews #JSM2021 @hglanz What do the job postings require these days? (This is how the content for the @CalPoly stat/data science program was developed.)
Read 64 tweets
#JSM2021 an exceptionally rare case of ACTUAL out of sample prediction in #MachineLearning #ML #AI: two rounds of the same health data collection by @CDCgov
@CDCgov Yulei He @cdcgov #JSM2021 RANDS 1 (fall 2015) + 2 (spring 2016): Build models on RANDS1 and compare predictions for RANDS2

ridge, lasso, elastic net, PLS, KNN, bagging, RF, GBM, XGBoost, SVM, deep learning
#JSM2021 Yulei He R-square about 30%; random forests and grad boosting reduce the prediction error by about 4%, shrinking towards the mean; standard errors are way to small (-50% than should be)
Read 4 tweets
#JSM2021 @jameswagner254 Using Machine Learning and Statistical Models to Predict Survey Costs -- presentation on the several attempts to integrate cost models into responsive design systems
#JSM2021 @jameswagner254 Responsive designs operate on indicators of errors and costs. Error indicators: R-indicator, balance indicators, FMI, sensitivity to ignorability assumptions (@bradytwest @Rodjlittle Andridge papers).
@jameswagner254 #JSM2021 @jameswagner254 Cost indicators? more difficult; proxies: # of attempts (Groves & Heeringa 2006)

Some decisions are made at the sample level (launch new replicate, switch to a new phase of the FU protocol), others at case level (change incentive amount, change mode)
Read 6 tweets
Now let's see how @olson_km is going to live tweet while giving her own #JSM2021 talk
@olson_km #JSM2021 @olson_km Decisions in survey design: questions of survey errors and questions of survey costs. Cost studies are hard: difficult to offer experimental variation of design features, with a possible exception of incentives. Observational examinations are more typical.
#JSM2021 @olson_km When you have one (repeated) survey at a time, you can better study the impacts of variable design features (but can't provide the basis for the features that do not vary.)
Read 12 tweets
#JSM2021 virtual vs. in-person: IMO there are exactly two activities at an average JSM that dictate in-person presence: cheering at the award ceremonies and browsing the new books. Confidential coffee (job search, editorial boards) can be done with burner phones.
Committee meetings should be /must be zoom calls; nobody is going back to in-person on that one. Having the presentations/files in advance/right after the event is the level of awesomeness not ever achieved by the conferences of the yester year.
Found yourself in a session that’s a poor match? Just click “All agenda” and find something else.
Read 4 tweets

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