Discover and read the best of Twitter Threads about #Statistical
Most recents (7)
Below is an on-going list of DD I have written. Each subject is organized into general topics with more DD tweeted and linked respectively. Any and all of my future analysis and research will be added to this pin.
The DD is never done.
#suckmyballshedgies
The DD is never done.
#suckmyballshedgies
$GME Cycles:
7/6/21: reddit.com/r/Superstonk/c…
7/7/21: reddit.com/r/Superstonk/c…
7/8/21: reddit.com/r/Superstonk/c…
8/1/21: reddit.com/r/Superstonk/c…
8/9/21:
reddit.com/r/Superstonk/c…
7/6/21: reddit.com/r/Superstonk/c…
7/7/21: reddit.com/r/Superstonk/c…
7/8/21: reddit.com/r/Superstonk/c…
8/1/21: reddit.com/r/Superstonk/c…
8/9/21:
reddit.com/r/Superstonk/c…
The #Algorithm. The Ouroboros: Characterizing #GameStop share price and reverse engineering #HFT #algos.
10/4/21: Part 1
reddit.com/r/DDintoGME/co…
11/18/21 Part 2
reddit.com/r/Superstonk/c…
10/4/21: Part 1
reddit.com/r/DDintoGME/co…
11/18/21 Part 2
reddit.com/r/Superstonk/c…
1/ Excited to share how T cell therapies kill #leukemia!! multi-omics + new #computational #singlecell tools for longitudinal analysis 👉unexpected answer! cell.com/cell-reports/f…
*👏* @elhamazizi! 🙏 @dpeer Cathy Wu @MDAndersonNews @CPRITTexas @ColumbiaBME @sloan_kettering
*👏* @elhamazizi! 🙏 @dpeer Cathy Wu @MDAndersonNews @CPRITTexas @ColumbiaBME @sloan_kettering
2/ We studied donor lymphocyte infusion (DLI) - an #Immunotherapy for relapsed #leukemia after #BMT & the #og of #celltherapy. Previously, we showed DLI reversed T cell exhaustion - but didn't know why/how/which T cells were responsible...
ashpublications.org/blood/article/…
ashpublications.org/blood/article/…
3/ To address these ?'s, we modeled intraleukemic T cell dynamics by integrating longitudinal, multimodal data from ~100K T cells (!) during response (R) or resistance (NR: nonresponder) to DLI.
This #meme shows an 'experiment' on #GMO 🌽.
It went #viral multiple times in the last decade.
Do we see a scientific study by 'students in England'❓
I found who took these pictures. How? 👇👇👇 1/..
#OSINT 🔎
#GeoLocation 🌎
#Verification 📸
#SpeurJeMee? 🧐 #HowToOSINT
It went #viral multiple times in the last decade.
Do we see a scientific study by 'students in England'❓
I found who took these pictures. How? 👇👇👇 1/..
#OSINT 🔎
#GeoLocation 🌎
#Verification 📸
#SpeurJeMee? 🧐 #HowToOSINT
We'll look for the original context 1⃣ AND we'll examine whether this 'experiment' meets the scientific standard 2⃣. 2/...
1/
@US_FDA approved the #Qcollar jugular compression device to "protect athletes' brains during head impacts."
This decision was based on VERY FLAWED DATA and does NOT demonstrate the safety or effectiveness of the Q-collar.
Read detailed thread⬇️
bit.ly/3q9ZZJL
@US_FDA approved the #Qcollar jugular compression device to "protect athletes' brains during head impacts."
This decision was based on VERY FLAWED DATA and does NOT demonstrate the safety or effectiveness of the Q-collar.
Read detailed thread⬇️
bit.ly/3q9ZZJL
2/
Here is the text from the @FDADeviceInfo press release.
If you don't understand the highly nuanced #MRI technique known as DTI, these results sound straightforward and convincing.
THEY ARE NOT. Don't be fooled by these numbers!🤔
I will dissect these in the thread below.
Here is the text from the @FDADeviceInfo press release.
If you don't understand the highly nuanced #MRI technique known as DTI, these results sound straightforward and convincing.
THEY ARE NOT. Don't be fooled by these numbers!🤔
I will dissect these in the thread below.
3a/
"No significant changes" is based AVERAGE response, not individual.
First, an easy-to-understand analogy below.
If half the sample experiences an increase and half experiences a decrease, they can cancel each other out to falsely suggest "no change" when one does exists!
"No significant changes" is based AVERAGE response, not individual.
First, an easy-to-understand analogy below.
If half the sample experiences an increase and half experiences a decrease, they can cancel each other out to falsely suggest "no change" when one does exists!
If the connotation of risk is an intertwined concept and is difficult to quantify, how does a Risk Officer look at it?
Is there any way other than using copula models to determine systemic risk with long tails or a black swan event?
@CQFInstitute @GARP_Risk @SOActuaries
Is there any way other than using copula models to determine systemic risk with long tails or a black swan event?
@CQFInstitute @GARP_Risk @SOActuaries
I guess we are worried about Market and Credit Risks or other interrelated financial risks which can create conjoint loss given events.
Any #Gaussian distribution model will enable you to model and predict potential Operational, Liquidity and Balance sheet AL - (Asset - liability) Mismatch, Market and Credit drove losses under normal market conditions.
I am into risk management.
Most of the risk managers are now required to have an advance background in operating technological applications such online trading and price data terminals (Bloomberg/Reuters, etc),
Most of the risk managers are now required to have an advance background in operating technological applications such online trading and price data terminals (Bloomberg/Reuters, etc),
FINTECH, Crypto Assets, Digital Marketing based vendor systems, DLT(Distributional Ledger Technologies) - Blockchain, AI / ALGO based trading in financial markets, Derivatives and Risk Pricing Engines and other Software Computational Programs,
Risk MIS/ERP Project Management Tasks, 4GL Fourth Generation Languages, Data Warehousing, BI, MI, SQL, NoSQL, and so on etc.
A Thread about the best test batsmen 1/25
#India #Aus #NZ #Stats #Cricket
A #statistical breakdown
Kane Williamson: 7115 runs @ 54.31, 24x100s
Virat Kohli: 7318 runs @ 53.41, 27x100s
Steve Smith: 7368 runs @ 61.91, 27x100s
Smith here has the best average by a fair way.
#India #Aus #NZ #Stats #Cricket
A #statistical breakdown
Kane Williamson: 7115 runs @ 54.31, 24x100s
Virat Kohli: 7318 runs @ 53.41, 27x100s
Steve Smith: 7368 runs @ 61.91, 27x100s
Smith here has the best average by a fair way.
But the joy of cricket is in its intricacies so let’s dive a little deeper. At home the 3 batsmen are absolute run machines.
Kane Williamson: 3788 runs @ 65.31 13x100s
Virat Kohli: 3558 runs @ 68.42 13x100s
Steve Smith: 3485 runs @ 68.33 14x100s
Kane Williamson: 3788 runs @ 65.31 13x100s
Virat Kohli: 3558 runs @ 68.42 13x100s
Steve Smith: 3485 runs @ 68.33 14x100s
Here their statistics are very similar.
However it is worth noting how hard it is to bat in each of these countries. These figures reflect the average runs of each players team mates in the last 10 years.
Australia: 44.27 runs/wicket
New Zealand: 42.28
India: 44.17
However it is worth noting how hard it is to bat in each of these countries. These figures reflect the average runs of each players team mates in the last 10 years.
Australia: 44.27 runs/wicket
New Zealand: 42.28
India: 44.17
