Applied statistics | Econometrics
Risk score (or risk scoring) is the name given to a general practice in applied statistics, bio-statistics, econometrics and other related disciplines, of creating an easily calculated number (the score) that reflects the level of risk in the presence of some risk factors (e.g. risk of mortality or disease in the presence of symptoms or genetic profile, risk financial loss considering credit and financial history, etc.). Risk scores are designed to be: * Simple to calculate: In many cases all you need to calculate a score is a pen and a piece of paper (although some scores use rely on more sophisticated or less transparent calculations that require a computer program). * Easily interpreted: The result of the calculation is a single number, and higher score usually means higher risk. Furthermore, many scoring methods enforce some form of monotonicity along the measured risk factors to allow a straight forward interpretation of the score (e.g. risk of mortality only increases with age, risk of payment default only increase with the amount of total debt the customer has, etc.). * Actionable: Scores are designed around a set of possible actions that should be taken as a result of the calculated score. Effective score-based policies can be designed and executed by setting thresholds on the value of the score and associating them with escalating actions. (Wikipedia).
QRM L1-1: The Definition of Risk
Welcome to Quantitative Risk Management (QRM). In this first class, we define what risk if for us. We will discuss the basic characteristics of risk, underlining some important facts, like its subjectivity, and the impossibility of separating payoffs and probabilities. Understanding the d
From playlist Quantitative Risk Management
QRM L1-2: The dimensions of risk and friends
Welcome to Quantitative Risk Management (QRM). In this second video, we analyse the dimensions of risk. Risk is in fact an object that we need to consider from different points of view, and that sometimes we cannot even quantify. We will also discuss the importance of statistical thinking
From playlist Quantitative Risk Management
What is Value at Risk? VaR and Risk Management
In todays video we learn about Value at Risk (VaR) and how is it calculated? Buy The Book Here: https://amzn.to/37HIdEB Follow Patrick on Twitter Here: https://twitter.com/PatrickEBoyle What Is Value at Risk (VaR)? Value at risk (VaR) is a calculation that aims to quantify the level of
From playlist Risk Management
Risk Management Lesson 5A: Value at Risk
In this first part of Lesson 5, we discuss Value-at-Risk (VaR). Topics: - Definition of VaR - Loss distribution and confidence level - The normal VaR
From playlist Risk Management
How do you calculate value at risk? Two ways of calculating VaR
In todays video we learn how to calculate VaR or Value at Risk. Buy The Book Here: https://amzn.to/37HIdEB Follow Patrick on Twitter Here: https://twitter.com/PatrickEBoyle What is VAR? The most popular and traditional measure of risk is volatility. The main problem with volatility, how
From playlist Risk Management
Welcome to Quantitative Risk Management (QRM). In this lesson we introduce the axiomatic approach to risk measures. We give the definition of risk measure and we discuss what its uses for us are in terms of reserve capital quantification. We then define coherent and convex measures. The p
From playlist Quantitative Risk Management
Risk Assessment: Likelihood Determination
http://trustedci.org/ Determining Likelihood of a threat as part of a cyber risk assessment.
From playlist Center for Applied Cybersecurity Research (CACR)
Confused about what a z-score is and how it relates to a bell curve? This short video explains in plain English what a z score is and what it's used for. Check out my Statistics Handbook: https://www.statisticshowto.com/the-practically-cheating-statistics-handbook/ Thanks for your support!
From playlist z-test
Please Subscribe here, thank you!!! https://goo.gl/JQ8Nys Definition of a Z-Score
From playlist Statistics
Stanford Webinar - How to Analyze Research Data: Kristin Sainani
In this webinar, Associate Professor Kristin Sainani walks you through the steps of a complete data analysis, using real data on mental health in athletes. She provides practical, hands-on tips for how to approach each step of the analysis and how to improve rigor and reproducibility of yo
From playlist Statistics and Data Science
Social Scores Are Real And You Have One Too
AI-derived scores rank individuals based on their profitability or risk as consumers, job candidates, or even defendants in court. Machine-learning algorithms decide your life. Support me through Patreon: https://www.patreon.com/thehatedone - or donate anonymously: Monero: 84DYxU8rPzQ88Sx
From playlist Decrypted Lies
What Your Family History Can’t Tell You
Thanks to the National Human Genome Research Institute for supporting this episode. If you’re interested in learning more about the human genome and the latest in polygenic risk score research, head to http://genome.gov/PRS #polygenicriskscores #science The first time you visit a new doc
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Ivana Išgum: "Deep learning for cardiovascular image analysis"
Deep Learning and Medical Applications 2020 "Deep learning for cardiovascular image analysis" Ivana Išgum - Amsterdam University Medical Center Abstract: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality worldwide. Medical imaging plays a crucial role in the de
From playlist Deep Learning and Medical Applications 2020
Automated Patient Risk Adjustment and Medicare HCC Coding from Clinical Notes
Presented by: Moritz Steller - Architect, AI + ML at Microsoft & Amir Kermany - Technical Director, Healthcare and Life Science at Databricks Medicare risk adjustment is a rule-based calculation, based on seven variables: ICD Codes of the patient's diagnoses, age, gender, eligibility segm
From playlist Healthcare NLP Summit 2022
Raising the bar on liquidity management
In this webinar, Axioma regulatory reporting and risk experts will explore the regulatory state of affairs as the investment industry readies itself for the SEC’s new, fast-approaching Modernization Regulations. Special focus will be paid to the topics of liquidity risk management and regu
From playlist Webinars: At home with the experts
Scoring systems: At the extreme of interpretable machine learning - Cynthia Rudin - Duke University
With widespread use of machine learning, there have been serious societal consequences from using black box models for high-stakes decisions, including flawed bail and parole decisions in criminal justice, flawed models in healthcare, and black box loan decisions in finance. Interpretabili
From playlist Interpretability, safety, and security in AI
IT Security Tutorial - Risk assessments and risk scores
Learn how risk assessments help to protect your organization's information systems from threats and vulnerabilities. Explore more IT Security courses and advance your skills on LinkedIn Learning: https://www.linkedin.com/learning/topics/security-3?trk=sme-youtube_M143014-68-01_learning&src
From playlist IT Security
IT Security Tutorial - Risk assessments and risk scores
Learn how risk assessments help to protect your organization's information systems from threats and vulnerabilities. Explore more IT Security courses and advance your skills on LinkedIn Learning: https://www.linkedin.com/learning/topics/security-3?trk=sme-youtube_M143014-68-01_learning&src
From playlist IT Security
Welcome to Quantitative Risk Management (QRM). In this lesson, we play with R to deal with VaR and ES. We show how to compute them empirically, but also in the case of normality. We then show that normality tends to underestimate tail risk, as observable in actual financial data. The pdf
From playlist Quantitative Risk Management