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.
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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.
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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!
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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…
#Statistics #DataScience 3/n
community.amstat.org/copss/awards/l…
#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.)
#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
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)
#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)
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)
#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.
