Tag: regression

Feed: R-bloggers. Author: R, Econometrics, High Performance. There are several ways to forecast tax revenue. The IMF Financial Programming Manual reviews 3 of them: (i) the effective tax rate approach; (ii) the elasticity approach; and (iii) the regression approach. Approach (iii) typically results in the most accurate short-term forecasts. The simple regression approach regresses tax revenue on its own lags and GDP with some lags. In the absence of large abrupt shifts in the tax base, domestic revenue can be assumed to have a linear relationship with GDP. Since however both revenue and GDP are typically non-stationary series, this relationship ... Read More

Feed: R-bloggers. Author: Learning Machines. We already covered Neural Networks and Logistic Regression in this blog. If you want to gain an even deeper understanding of the fascinating connection between those two popular machine learning techniques read on! Let us recap what an artificial neuron looks like: Mathematically it is some kind of non-linear activation function of the scalar product of the input vector and the weight vector. One of the inputs the so-called bias (neuron), is fixed at 1. The activation function can e.g. be (and often is) the logistic function (which is an example of a sigmoid function): ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. There is no statistical test that assesses whether a sequence of observations, time series, or residuals in a regression model, exhibits independence or not. Typically, what data scientists do is to look at auto-correlations and see whether they are close enough to zero. If the data follows a Gaussian distribution, then absence of auto-correlations implies independence. Here however, we are dealing with non-Gaussian observations. The setting is similar to testing whether a pseudo-random number generator is random enough, or whether the digits of a number such as π behave in a ... Read More

Feed: SAS Blogs. Author: Rick Wicklin. A SAS customer asked a great question: "I have parameter estimates for a logistic regression model that I computed by using multiple imputations. How do I use these parameter estimates to score new observations and to visualize the model? PROC LOGISTIC can do the computation I want, but how do I tell it to use the externally computed estimates?" This article presents a solution for PROC LOGISTIC. At the end of this article, I present a few tips for other SAS procedures. Here's the main idea: PROC LOGISTIC supports ... Read More

Feed: R-bloggers. Author: Posts on R Lover ! a programmer. My last 4 posts have all focused on thevaccines being produced to fight COVID-19. They have primarily focused onBayesian methods (or at least comparing bayesian to frequentist methods). Thisone follows that pattern and provides expanded coverage of the concept ofpriors in bayesian thinking, how to operationalize them, and additionalcoverage of how to compare bayesian regression models using various tools inr. There’s no actual additional analysis of “real data”. While the news has allbeen good lately I haven’t found anything publicly available that begs forinvestigation. Instead we’ll use “realistic” but not ... Read More

Feed: Planet PostgreSQL. In the last two blog posts on data science in Postgres, we got our data ready for regression analysis and had predictive variables that are on wildly different scales. Another example of data on different scales would be annual income versus age. The former is usually at least tens of thousands while age rarely gets to a hundred. If you do the regression with non-transformed variables, it becomes hard to compare the effect of the different variables. Statisticians account for this by converting raw data values into a Z-score for each explanatory variable. Other names for this technique ... Read More

Feed: R-bloggers. Author: Keith Goldfeld. [This article was first published on ouR data generation, 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. This is the context. In the convalescent plasma pooled individual patient level meta-analysis we are conducting as part of the COMPILE study, there is great interest in understanding the impact of antibody levels on outcomes. (I’ve described various aspects of the analysis in previous posts, most recently here). In other words, ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Avinash Udaykumar. Consistent asset health across many levels from cell sites to regions is critical to ensure uninterrupted operations for telecom operators. However, proactively identifying anomalous patterns like equipment malfunction remains a major challenge. Drawing on Subex’s deep implementation experience, this paper describes the chief steps necessary to deploy successful pattern detection solutions. It also examines some high impact use cases for pattern detection that have systematically delivered value for telecom carriers. High operational efficiency is an important differentiator for telecom operators establishing their brand reputation on consistency and service availability ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Vincent Granville. The model-free, data-driven technique discussed here is so basic that it can easily be implemented in Excel, and we actually provide an Excel implementation. It is surprising that this technique does not pre-date standard linear regression, and is rarely if ever used by statisticians and data scientists. It is related to kriging and nearest neighbor interpolation, and apparently first mentioned in 1965 by Harvard scientists working on GIS (geographic information systems). It was referred back then as Shepard's method or inverse distance weighting, and used for multivariate interpolation on ... Read More

Feed: SAS Blogs. Author: Rick Wicklin. I previously showed how to create a decile calibration plot for a logistic regression model in SAS. A decile calibration plot (or "decile plot," for short) is used in some fields to visualize agreement between the data and a regression model. It can be used to diagnose an incorrectly specified model. In principle, the decile plot can be applied to any regression model that has a continuous regressor. In this article, I show how to create two decile plots for an ordinary least squares regression model. The first overlays ... Read More