9月 142016

Clay Barker has been busy extending the usefulness of the Generalized Regression platform in JMP Pro, adding many new models and enhancing ease of use. Generalized Regression (or GenReg for short) debuted in JMP Pro 11 as the place to do a trio of popular penalized regression techniques: Lasso, Elastic Net and Ridge. These penalized techniques are attractive because they lead to simpler models that are less prone to overfitting. After adding more modeling and selection techniques in JMP Pro 12 and now JMP 13, GenReg has become a place to do variable selection quickly and easily for a wide variety of problems.

Variable selection is where much of the “art” is in model building, and even more so with ever-wider data.

“Customers have been asking for variable selection for time-to-event data for some time, and now GenReg will be able to do that. There are not a lot of easy options for doing variable selection across so many different scenarios” says Clay, a Senior Research Statistician Developer at JMP.

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Now you can use Generalized Regression on time-to-event data.

One of the biggest enhancements in the Generalized Regression platform for JMP Pro 13 is the ability to handle censored data to do parametric survival analysis and proportional hazards models. Fellow developer Peng Liu even added a link to the Generalized Regression platform from within the Survival Analysis platform (in JMP Pro).

Variable selection is an active area of research in statistics. To do variable selection well and build really useful models, you need a breadth of tools and even hybrid approaches using automated selection techniques like the Lasso and Elastic Net. But the interactive nature of GenReg makes it easy to adopt a hybrid strategy where you can easily explore alternative models supported by the automated selection technique. Now JMP Pro provides some powerful new variable selection methods, including a modified two-stage forward selection method — first on the main effects and then on the higher-order effects involving the main effects, making Generalized Regression a premier tool for analyzing designed experiments.

Also new in JMP Pro 13 is the Double Lasso, a two-stage modeling technique where a first pass of the Lasso screens for variables to select and then a second pass of the Lasso is done on the variables selected in the first pass. Doing two passes of the lasso effectively separates the selection and shrinkage process of the Lasso, which can lead to better predictions. Another highlight is the addition of the Extended Regularized Information Criterion (ERIC). ERIC is similar in spirit to the Bayesian Information Criterion, but it was derived specifically for the Adaptive Lasso.

You can find out more about what’s coming in JMP Pro 13 by visiting the preview page on our website. There, you can sign up to watch a live stream of JMP chief architect John Sall’s tour of JMP 13 on Sept. 21, as well as watch short videos about JMP 13 and JMP Pro 13.

tags: Generalized Regression, JMP 13, JMP Pro, Statistics

The post JMP 13 Preview: More enhancements to generalized regression appeared first on JMP Blog.

9月 092016

Building on the new features in JMP 13 for exploring unstructured text data, JMP Pro 13 enables you to do more with text data, like cluster terms and phrases and use text in predictive models. You’ll be able to answer more questions, scale to larger data and stay in flow. […]

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1月 072016

Are you using JMP 12 or JMP Pro 12? If so, please read on.... A maintenance update for JMP 12 and JMP Pro 12 is now available, and it’s recommended for all users and sites. What's in JMP 12.2? JMP 12.2 includes bug fixes and a few new features, including: […]

The post It's time to update your copy of JMP 12 and JMP Pro 12 appeared first on JMP Blog.

10月 272015

The days are getting shorter, and the weather is getting cooler. That means that my favorite time of year is almost here: basketball season. Having spent most of my life on Tobacco Road, I'm not sure that I had any choice but to love playing and watching the game. As […]

The post Ranking basketball teams, using Generalized Regression appeared first on JMP Blog.

9月 222015

One of the key application areas for JMP is consumer and market research. As I was invited to give a presentation on JMP for marketing analytics, I was curious about how complete the capabilities of JMP are. I wondered whether there are any significant gaps. At a recent American Marketing Association conference […]

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9月 162015

SAS co-founder and JMP creator John Sall gives a keynote speech at Discovery Summit 2015 in San Diego, California, titled "A Few of My Favorite Things." Sall takes a deep dive into a few aspects of what he likes best about JMP and JMP Pro. You may be surprised and […]

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9月 012015

“When building a predictive model, we find the JMP Pro interfaces to be very intuitive, allowing us to work closely with other JMP Pro users to build the model together.” -- Amy Clayman, Data-Driven Decisions Circle, VCE Beyond Spreadsheets is a blog series that highlights how JMP customers are augmenting […]

The post Beyond Spreadsheets: Amy Clayman, Voice Systems Engineering appeared first on JMP Blog.

3月 312015

The documentation for JMP must meet the needs of JMP users with a diverse set of backgrounds. The needs of one group of users can differ markedly from the needs of another group. For instance, some users report that there is too much statistical jargon in the documentation, while others […]

The post What statistical details do you want documented? appeared first on JMP Blog.

3月 182015
JMP 12 arrives next week, and I hope you've had a chance to read the series of posts by JMP developers about what's coming in this new version. I’ve been using JMP 12 during the entire development cycle (about 18 months now), and I am impressed how this version has […]