Tag: regression

Feed: R-bloggers. Author: R Views. [This article was first published on R Views, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. One hundred forty-six new packages stuck to CRAN in August. Below, are my “Top 40” picks in eleven categories: Computational Methods, Data, Genomics, Insurance, Machine Learning, Mathematics, Medicine, Statistics, Time Series, Utilities and Visualization. Computational Methods dpseg v0.1.1: Implements an algorithm for piecewise linear segmentation of ordered data by a dynamic programming ... Read More

Feed: R-bloggers. Author: Marcin Kosiński. [This article was first published on http://r-addict.com, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. Why R 2020 Keynotes sessions are close! Check out below posts to find out dates and descriptions of keynote talks for 2020.whyr.pl. Why R? 2020 Keynotes Julia Silge is a data scientist and software engineer at RStudio PBC where she works on open source modeling tools. She is an author, an international keynote ... Read More

Feed: R-bloggers. Author: Jakub Wiśniewski. Version 0.2.2 is out and it has big changes! TL;DR Some new functionalities were added in R package fairmodels which is available on CRAN. In this blog, I will cover them in depth. The biggest changes concerned the fairness_check() function, parity_loss, and the addition of a new plot (not really but it hasn’t been properly introduced). Problem The boundary between fair and discriminatory decision is really arbitrary and subjective. Let’s say that people from towns A and B are identical and the amount of people applying for loans is big and equal in both towns. Can ... Read More

Feed: AWS Partner Network (APN) Blog. Author: Néstor Gándara. By Néstor Gándara, Partner Solutions Architect at AWSBy Piyush Agarwal, AWS Ambassador at VirtusaVirtusa recently assisted a provider-sponsored health insurance company that insures 1.2 million members in New York City and downstate New York in migrating their Pega 7.2 environment on Pega Cloud to Pega 8.4 on Amazon Web Services (AWS). Over the past several years, Virtusa’s client has been leveraging Pega’s cloud services to develop and operate production Pega applications. With the continued development and growth of the applications and data in a large single environment, it became overly difficult ... Read More

Feed: SAS Blogs. Author: Rick Wicklin. A previous article discussed how to solve regression problems in which the parameters are constrained to be a specified constant (such as B1 = 1) or are restricted to obey a linear equation such as B4 = –2*B2. In SAS, you can use the RESTRICT statement in PROC REG to solve restricted least squares problems. However, if a constraint is an INEQUALITY instead of an EQUALITY, then (in general) a least squares method does not solve the problem. Therefore, you cannot use PROC REG to solve problems that have inequality constraints. This ... Read More

Feed: SAS Blogs. Author: Marcia Surratt. Many examples for performing analysis of covariance (ANCOVA) are available in statistical textbooks, conference papers, and blogs. One of the primary interests for researchers and statisticians is to obtain a graphical representation for the fitted ANCOVA model. This post, co-authored with my SAS Technical Support colleague Kathleen Kiernan, demonstrates how to obtain the statistical analysis for a quadratic ANCOVA model by using the GLM procedure and graphical representation by using the SGPLOT procedure. For the purposes of this blog, the tests for equality of intercepts and slopes have been omitted. For more detailed information ... Read More

Feed: SAS Blogs. Author: Rick Wicklin. A data analyst recently asked a question about restricted least square regression in SAS. Recall that a restricted regression puts linear constraints on the coefficients in the model. Examples include forcing a coefficient to be 1 or forcing two coefficients to equal each other. Each of these problems can be solved by using PROC REG in SAS. The analyst's question was, "Why can I use PROC REG to solve a restricted regression problem when the restriction involves EQUALITY constraints, but PROC REG can't solve a regression problem that involves an INEQUALITY constraint?" ... Read More

Feed: SAS Blogs. Author: Yiu-Fai Yung. A note from Udo Sglavo: In Digital transformation, scientific computing, and peace of mind, I mention that the COVID-19 pandemic is paralyzing the world. However, new challenges are also inspiring new ideas to tackle those challenges. We might ask questions about what is causal in nature, trying to figure out relationships among various aspects of life. But hang on: didn’t we learn that correlation does not imply causation? In this post, Yiu-Fai Yung, Senior Manager in Scientific Computing R&D, introduces causal analysis techniques and illustrates how we can understand and evaluate the impact of ... Read More

Feed: R-bloggers. Author: Economics and R - R posts. An exciting, quite new branch of econometrics studies how machine learning techniques can be adapted to consistently estimate causal effects. For example, the widely cited article by Belloni, Chernozhukov and Hansen (2014) introduces a post double selection method where one first runs two lasso regressions to select suitable control variables for a final OLS regression.When I first heard about the method, it had the alluring promise to provide an objective way to select control variables that may reduce the scope of p-hacking. Under certain assumption Belloni et. al. establish consistency and ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Niranjan. Data science is a collective pool of various algorithms, tools, machine learning principles that work in unison to extract hidden patterns from raw data. It requires a diverse set of skills and demands knowledge from aspects of mathematics, science, communication, and business. Honing a diverse skill set, data scientists gain the ability to analyze numbers and influence decisions. The core objective of data scientists lay in bridging the gap between numbers and actions by using information to affect real world decisions. This demands excellent communication skills along with understanding the ... Read More