SAS/STAT offers a principled treatment of missing data in a frequentist paradigm. SAS' Michael Senter reveals how with a few code tweaks, you can seamlessly transition to a Bayesian analysis.
Many people have an intuitive feel for residuals in least square models and know that the sum of squared residuals is a goodness-of-fit measure. Generalized linear regression models use a different but related idea, called deviance residuals. What are deviance residuals, and how can you compute them? Deviance residuals (and
SAS의 업계 전문가는 보험업계의 불확실성 속에서도 새로운 기술과 신뢰가 보험사를 주도할 것으로 전망합니다. 보험사들이 기후 변화의 영향으로 조심스러운 행보를 보이고 있는 가운데, 차세대 보험 전문가들이 업계를 이끌어 나갈 것으로 예측됩니다. SAS 전문가들은 2025년이 현대 보험사의 해가 될 것이며, 혁신을 추진하는 보험사들이 업계 중점 과제들에 과감히 맞서며 향상된 속도와 생산성, 신뢰할
A previous article describes how to use SAS to find the inflection points of a 1-D function that you can evaluate at any point. The function must be given by a formula (or by an algorithm) because the root-finding algorithm needs to evaluate the function at arbitrary locations. However, sometimes
업계 탑 애널리스트 평가 결과, SAS 바이야(Viya)는 AI/ML 개발 및 의사 결정의 모든 단계에서 리더십을 입증했습니다. 최고의 애널리스트 회사들은 오랫동안 SAS와 SAS 기술력의 우수성을 인정해 왔습니다. 이러한 높은 평가는 2024년에도 변함이 없었습니다. SAS와 SAS 바이야 플랫폼은 지난해에도 AI/ML 개발 및 의사 결정 부문의 리더임을 다시 입증 받았습니다. 실제로 SAS는 IDC,
A SAS programmer asked if it is possible to numerically find an inflection point for a univariate function, f(x). Yes! This can be solved as a variation of a classic numerical root-finding problem. Recall that an inflection point is a value (call it x0) in the domain where the graph
There is no question that organizations worldwide are increasing their investment in AI. There is also little doubt that AI is starting to impact many different sectors. The health care and life sciences sectors are no exception, with many organizations investing in new technology. The real issue is how to
I previously wrote an article about the Lambert W function. The Lambert W function is the inverse of the function g(x) = x exp(x). This means that you can use it to find the value of x such that g(x)=c for any value of c in the range of g, which
SAS's Ann Kuo walks you through how SAS Tech Support developed an email classifier to clean up spam and misaddressed emails using SAS Viya's NLP-based text classifier
A SAS programmer had many polynomials for which he wanted to compute the real roots. By the Fundamental Theorem of Algebra, every polynomial of degree d has d complex roots. You can find these complex roots by using the POLYROOT function in SAS IML. The programmer only wanted to output
Here's a SAS tip for you. Most SAS programmers know that SAS provides syntax that makes it easy to specify a list of variables. For example, you can use the hyphen and colon operators to specify lists of variables on many SAS statements: You can use the hyphen operator (-)
SAS' Sophia Rowland breaks down the roles of each team member in a long-term machine learning project and how they can better combine their efforts to increase efficiency and efficacy
A colleague asked me an interesting question: Suppose you have a structured correlation matrix, such as a matrix that has a compound symmetric, banded, or an AR1(ρ) structure. If you generate a random correlation matrix that has the same eigenvalues as the structured matrix, does the random matrix have the
In a previous article, I presented some of the most popular blog posts from The DO Loop in 2024. In general, popular articles deal with elementary topics that have broad appeal. However, I also write technical articles about advanced topics, which typically do not make it onto a Top 10
AI is increasingly prevalent in our daily lives, and this trend is unlikely to change anytime soon. This comes with risks, but by understanding these risks, we can build AI systems that mitigate them.
A previous article discusses covariance matrices that have the same set of eigenvalues. The set of eigenvalues is called the spectrum of the matrix. For symmetric matrices, the spectrum contains real numbers. For covariance matrices, which are positive semidefinite, the eigenvalues are nonnegative. It turns out that two symmetric matrices
Rising fraud and shrinking staff signal an urgent need for AI in government Have you ever opened your wallet and felt panicked? You count the bills once, twice and still come up short. You rummage through every pocket, hoping to find that missing cash. Then, a thought crosses your mind,
In 2024, I wrote about 80 articles for The DO Loop blog. My most popular articles were about SAS programming, data visualization, and statistics. If you missed any of these articles, here is the "Reader's Choice Awards" for some of the most popular articles from 2024! SAS Programming The following
We all know by now that customer experience (CX) is pivotal to any organization's success. Providing seamless, secure, personalized interactions across all customer touchpoints can be transformative. To achieve this, organizations must move beyond siloed functions such as fraud detection and credit risk management, integrating these critical areas with broader
금융기관은 다양한 업무에 조직과 더불어 여러 시스템들이 갖춰져 있습니다. 그러한 시스템에는 수많은 프로그램과 모델(로직)들이 결합되어 업무 활용의 효율성과 신뢰성을 보완해주는 역할을 합니다. 특히나 AI 모델을 많이 만들고 사용하는 최근에는 그동안 기업에서 각종 규제에 맞춰 만들어진 모델들까지 포함한 전사적 모델 관리가 리스크 관리의 중요한 부분으로 떠오르고 있습니다. 여기서, 여러분이 컴플라이언스/리스크 관리
注） 本コラムは『経時的に変化する治療（Time-varying treatments）に対する因果推論』と題した以前のコラムを、時間依存性治療に関する部分と周辺構造モデルにおけるIPTW法に関する部分に分割し、内容の追加と修正を行い再構成したものの一部となります。 はじめに 以前のコラムでは、「時間依存性治療とはなにか」、「時間依存性治療の因果効果はどのように定義されるのか」、「定義した因果効果はどう推定すれば良いか」について紹介しました。時間依存性治療の因果効果の推定にあたっては、一般に条件付けに基づく手法（e.g., 回帰、層別化、マッチング）は不適であり、g-methods※1と総称される推定手法が広く用いられています。本コラムでは、それらの中でも直感的な理解や実装が最も容易である「周辺構造モデルにおけるIPTW法（inverse probability of treatment weighting (IPTW) of marginal structural models (MSMs)」の理論とSASでの実装方法について簡単に紹介します。コラム全体の流れは以下の通りです。 時間固定性治療（time-fixed treatments）※2に対する周辺構造モデルとIPTW法の紹介 IPTW法の概要 周辺構造モデルの設定がなぜ必要か 時間依存性治療（time-varying treatments）に対する周辺構造モデルとIPTW法の紹介 SASでの実装 まとめ なお、本コラムは統計的因果推論に関する基本的な理解があることを前提としております。また、文献や書籍によっては、IPTW（Inverse probability of treatment weighting）は、単にIPW（Inverse probability weighting）と記載される場合もあります。しかし、IPW（逆確率重み付け）は治療効果の直接的な推定を目的とした治療変数に関する重み付け以外にも、打ち切りに対する補正（i.e., 打ち切り変数に関する重み付け）等でも用いられることがあり、本コラムでは前者であることを強調するためにIPTWと記載します。加えて、本コラムでは連続もしくは二値であるアウトカム（結果変数）が、研究最終測定時点でのみ測定される状況を想定します。アウトカムが生存時間（time-to-event）である場合や各時点の治療実施後に繰り返し測定される場合など※3、異なる状況における議論についてはreferenceにある文献等をご参照いただくか、著者宛に別途ご連絡いただけると幸いです。 ※1 (i) Inverse probability of treatment weighting of marginal structural models（周辺構造モデルにおけるIPTW法）、(ii) g-computation algorithm formula（"g-formula"）、(iii) g-estimation of stractural nested model（構造ネストモデルにおけるg-estimation）のという3手法の総称
SAS' Natalie Patten shows you the potential of integrating a country classification transformer into data cleansing workflows.
A previous article discusses how to generate a random covariance matrix with a specified set of (positive) eigenvalues. A SAS programmer asked whether it is possible to produce a correlation matrix that has a specified set of eigenvalues. After discussing the problem with a friend, I am happy to report
SAS' Mary Osborne, Ali Dixon Ricke, and Franklin Manchester break down what insurers still need to learn about generative AI.
O Christmas tree, O Christmas tree, How lovely are your branches! SAS programmers have a long history of creating yuletide-themed graphics. Christmas trees are a popular image because of their simplicity. I admit that I have indulged more than once in this holiday tradition: An old-school ASCII art image A
Review these three cool use cases for SAS users, built using SAS Viya Workbench. SAS and Python for better working together!
As populations around the world the U.S. continue to grow, both condensing in urban areas and sprawling in more rural areas, the importance of a functioning distribution network for utilities grows proportionally. The complexity and interconnected nature of say, the electric distribution network, is already staggering; one can hardly imagine
AI/ML 모델 개발 상의 어려움과 이를 해결하기 위한 접근법으로서 ModelOps의 필요성이 대두되고 있습니다. (참조 : AI/ML 기반 모델 개발, 과제와 해결방안은?) 이번 글에서는 ModelOps가 구체적으로 어떤 제품인지, 어떤 장점을 제공하며 구현방법은 어떠한지 등에 대해 설명드리도록 하겠습니다. 이에 앞서 ModelOps의 구현에 중요한 역할을 하는 ‘모델 거버넌스’에 대해 잠깐 짚어보도록 하겠습니다. 모델
SAS' Shawn Romero introduces you to the SAS Payment Integrity for Food Assistance model.
SoDA를 이용해 쉽게 배우는 데이터 과학 #4 지난 포스팅에서는 SoDA 인터페이스 구성과 그 기능에 대해 알아보았습니다. 오늘은 SoDA 인터페이스 중 ‘작업 모드’와 ‘프로세스 플로우’ 두 가지 요소에 대해 알아보겠습니다. 이 두 요소는 코딩에 익숙한 사용자와 코딩이 낯선 사용자 모두 쉽게 사용할 수 있도록 구성되어 있습니다. 이제부터 각각을 자세히 살펴보겠습니다. 1.
