A previous article discusses a "Catch-22" paradox for fitting nonlinear regression models: You can't estimate the parameters until you fit the model, but you can't fit the model until you provide an initial guess for the parameters! If your initial guess for the parameters is not good enough, the nonlinear
Think of data engineering as plumbing for the digital world. All those smart dashboards, AI models and analytics reports don’t work unless someone builds the pipelines that bring clean, usable data to them. That someone is a data engineer. Why data engineers are suddenly in the spotlight AI is everywhere today
This guide explains how businesses can successfully implement generative AI by focusing on narrow use cases, curating data, leveraging AI agents, safeguarding sensitive information, monitoring for bias and toxicity, and ensuring model accuracy and relevancy.
I have previously written about the moment-ratio diagram as a graphical tool for modeling univariate distributions and also as a tool for examining the distribution of the skewness and kurtosis statistics for distributions. The simple moment-ratio (M-R) diagram from my book (Wicklin, 2013, Simulating Data with SAS, Chapter 16) is
How I collected birdsong detection data from BirdNET-PI and used SAS to visualize the bird visits across the seasons.
AI는 비즈니스와 기술 분야에서 여전히 가장 뜨거운 주제이지만, 아직 AI를 효과적으로 활용하여 가치를 창출하는 데에는 어려움이 많습니다. 여기 여러분의 능력을 발휘할 기회가 있습니다. 제 5회 'SAS 해커톤'에 참여하시고 최신 AI 기술을 활용한 가치 창출에 도전해 보세요! *SAS 해커톤 참가등록 : 등록 링크* 지금부터 8월 31일까지 등록할 수 있는 이번 해커톤은
La gestión de riesgos ha evolucionado de ser una función meramente defensiva a convertirse en un motor estratégico clave para el crecimiento sostenible. Ya no se trata solo de proteger el valor existente, sino de identificar y aprovechar nuevas oportunidades que, si se gestionan adecuadamente, pueden afectar a las organizaciones
은행업계의 미래는 지금보다 지능화되겠지만 위험도는 더 높아질 것이라는 연구결과가 발표되어 업계의 이목을 끌고 있습니다. SAS의 후원을 받아 이코노미스트 임팩트(Economist Impact)가 진행한 새로운 연구에서는 은행업계가 직면해 있는 선택지와 앞으로 나아갈 방향을 짚어보고, '디지털 인텔리전스 시대'를 선도하기 위한 5가지 필수 요소를 제시합니다. 전 세계 금융 기관은 심화되는 경제적 변동성, 폭발적인 기술 가속화
SAS가 고객 여러분에게 더 가까이 다가가기 위해 지난 6월 10일 여의도 페어몬트 앰배서더 서울에서 'SAS 이노베이트 온 투어' 행사를 개최했습니다. 이 행사는 지난 5월 미국 플로리다주 올랜도에서 열린 SAS의 연례 컨퍼런스 ‘SAS 이노베이트 2025(SAS Innovate 2025)’의 핵심 내용을 각국의 고객들께 직접 전해드리기 위해 마련된 자리로서, 올해는 서울을 포함한 15개 주요
Many of today’s fraudsters are figuring out how to use AI to automate and structure scams that are unique to each person they target. If fraudsters can analyze your data, learn your patterns, track your interests, and exploit who you trust – how can you combat them? According to SAS
This article shows how to classify a set of high-dimensional data into orthants. An orthant is the d-dimensional generalization of a quadrant. For 2-D Euclidean space, there are four quadrants, often labeled by Roman numerals I-IV. The quadrants are open sets that are defined by the signs of each coordinate
Authors: Bahar Biller, Jagdishwar Mankala, and Jinxin Yi Managing spare parts inventory is a critical aspect of asset performance management, especially in industries where equipment downtime is costly. This post, based on a real-world project with a major aircraft manufacturer, explores how to optimize spare parts inventory under uncertainty. We
This article shows how to use SAS to implement the ISO algorithm to generate (and validate) a Universal Loan Identifier (ULI). A ULI is a long string of numbers and letters that serves as a unique identifier that is used for certain financial transactions. The ISO standard ensures that banks
여러분이 생각하시는 데이터와 AI의 미래는 어떤 모습인가요? 생성형 AI에서 양자 컴퓨팅까지, 지금껏 상상해왔던 수많은 기술과 개념이 지금 이 순간 현실로 다가오고 있습니다. 불가능을 가능으로, 상상을 현실로 만들 데이터와 AI의 첨단 화두가 무엇인지 궁금하시다면 SAS Innovate 2025에서 확인해 보세요. 5월 6-9일까지 나흘간 미국 플로리다주 올랜도에서 개최된 ‘SAS 이노베이트 2025(SAS Innovate 2025)’는
On social media, a SAS user reported that SAS could not compute the modulo of an extremely large integer. In SAS, the modulo operation is usually performed by using the MOD function, which computes the remainder of dividing an integer, N, by another integer, d. (In symbols, the remainder is
Enforce SAS code standards using SASjs and GitHub Actions to maintain secure, readable, and maintainable code. Automated linting blocks non-compliant code from merging into protected branches.
A previous article discusses the Gini-Simpson diversity index and how to compute it in SAS. Suppose you have a sample that contains R classes. (Classes are also called groups or categories.) Intuitively, the sample exhibits "high diversity" if the class sizes are approximately equal. The sample shows "low diversity" if
Accurately identifying lag structures between related time series is essential in public health forecasting, particularly during epidemics where delays between infections and hospitalizations affect planning. Using a simulated SEIR model and SAS Viya’s PROC TSSELECTLAG, distance correlation is shown to outperform Pearson correlation by correctly identifying nonlinear lag relationships—such as the true seven-day lag between new infections and hospital admissions.
An article by David Corliss in Amstat News (Corliss D. (2025) "Quantifying Diversity: Calculating the Gini-Simpson Diversity Index") discusses a new statistical measure of diversity that was adopted by the US Census Bureau. The statistic is called the Gini-Simpson diversity index. The Census Bureau has published an article about how
If you have a background in pharmacy, life sciences, biotechnology, or statistics – and you’re curious about the world of clinical research – there’s one career path you might want to explore: clinical SAS programming. Maybe you’ve heard the term before and wondered what it really involves. The good news?
SAS announces continued support and releases for SAS 9 and a new role for SAS Enterprise Guide with SAS Viya.
When you use the bootstrap method in statistics, the most common resampling method is called case resampling. For data that has N observations, each bootstrap sample is created by sampling with replacement from the N observations (or "cases") in the data. However, if the data set includes categorical variables, it
A recent article describes the main features of simulation by using the Synthetic Minority Over-sampling Technique (SMOTE). SMOTE was created to oversample from a set of rare events prior to running a machine learning classification algorithm. However, at its heart, the SMOTE algorithm (Chawla et al., 2002) provides a way
The financial services industry is undergoing a period of profound change, driven by a dynamic economic landscape, increased regulatory scrutiny, changing consumer behavior and rapid technological advances. Banks operating in this environment are under increasing pressure to transform their risk modeling and decision-making ecosystems in order to remain competitive. This
The Synthetic Minority Over-sampling Technique (SMOTE) was created to address class-imbalance problems in machine learning algorithms. The idea is to oversample from the rare events prior to running a machine learning classification algorithm. However, at its heart, the SMOTE algorithm (Chawla et al., 2002) is essentially a way to simulate
What insurance companies need to know about the Fundamental Rights Impact Assessment (FRIA) The EU Artificial Intelligence Act (EU AI Act) is ushering in a new era of accountability and transparency for organizations deploying AI systems – particularly in high-impact sectors like insurance. The Fundamental Rights Impact Assessment (FRIA) is
As agentic AI systems evolve through protocols like MCP and A2A, traditional security practices must be adapted to address new risks such as goal misalignment and tool instruction abuse. This article explores practical threat modeling strategies, including goal alignment cascades and distinguishing between parameter-only vs. instruction-enabled tool calls.
The financial industry is evolving at an unprecedented pace. Regulatory frameworks are becoming more complex, customer expectations are shifting and financial risks are emerging in new and unexpected ways. Because of this, relying on outdated risk management systems is no longer sustainable. Banks must adopt data-driven strategies to remain competitive
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SAS programmers love to brag that the SAS will still run a program they wrote twenty or forty years. This is both a blessing and a curse. It's a blessing because it frees the statistical programmer from needing to revisit and rewrite code that was written long ago. It's a
