A previous post, Spatial econometric modeling using PROC SPATIALREG, introduced the SAS/ETS® SPATIALREG procedure and demonstrated its usage to fit both linear and SAR models by using 2013 county-level home value data in North Carolina. In most analysis for spatial econometrics, you rarely know the true model from which your data
In honor of today’s #GivingTuesday, which "harnesses the potential of social media and the generosity of people around the world to bring about real change in their communities,” I’ve been thinking about what constitutes “real change” and the role analytics can play on the many social issues our planet faces.
Who says machine learning can't be fun? A crew of us from SAS went to San Francisco for the recent KDD conference, which bills itself as "a premier interdisciplinary conference, [which]brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data." We brought
The internet of medical things, spurred by the advent of wearable sensors, has dramatic consequences in industry, healthcare, and analytics, just as the advent of the internet of things and analytics has consequences in education. When I began my internship at SAS in May, I knew little about the internet of
Time series machine learning techniques show great promise for the analysis of health care wearable data. As our busy lifestyles render continuous monitoring more and more essential, the need to analyze data to find correlations between these data streams becomes even more important, because they can provide important cues to
The annual SAS Analytics Conference is upon us again. This year it is known by a different name, Analytics Experience 2016, but the location, Las Vegas, is the same as it has been the previous two years. Just like last year, I will be attending and presenting on analytics for panel
In our previous post, Econometric and statistical methods for spatial data analysis, we discussed the importance of spatial data. For most people, understanding that importance is relatively easy because spatial data are often found in our daily lives and we are all accustomed to analyzing them. We can all relate to
Asking about the benefits of artificial intelligence and machine learning reminds me a little of the transition to suitcases with wheels. Do you remember lugging around those old suitcases? If not, good for you - this original advertisement from US Luggage will take you back! Thank Bernard Sadow for persistence with his
Machine learning applications for NBA coaches and players might seem like an odd choice for me to write about. Let us get something out of the way: I don’t know much about basketball. Or baseball. Or even soccer, much to the chagrin of my friends back home in Europe. However,
Optimization for machine learning is essential to ensure that data mining models can learn from training data in order to generalize to future test data. Data mining models can have millions of parameters that depend on the training data and, in general, have no analytic definition. In such cases, effective models
