Month: July 2012

Leave a comment

Launched new book website for Practical Forecasting book

gshmueli

Last week I launched a new website for my textbook Practical Time Series Forecasting. The website offers resources such as the datasets used in the book, a block with news that pushes posts to the book Facebook page, information about the book and author, for instructors an online form for requesting an evaluation copy and another for requesting access to solutions, etc. I am already anticipating my colleagues’ question “what platform did you use?”. Well, I did not hire a web designer, nor did I spend three months putting the website together using HTML. Instead, I used Google Sites. This … Continue reading Launched new book website for Practical Forecasting book

Leave a comment

Explain/Predict in Epidemiology

gshmueli

Researchers in various fields have been sending me emails and reactions after reading my 2010 paper “To Explain or To Predict?“. While I am aware of research methodology in a few areas, I’m learning in more detail about the scientific challenges caused by “predictive-less” areas. In an effort to further disseminate this knowledge, I’ll be posting these reactions in this blog (with the senders’ approval, of course). In a recent email, Stan Young, Assistant Director for Bioinformatics at NISS, commented about the explain/predict situation in epidemiology: “I enjoyed reading your paper… I am interested in what I think is [epidemiologists] lack … Continue reading Explain/Predict in Epidemiology

2 Comments

Linear regression for binary outcome: even better news

gshmueli

I recently attended the 8th World Congress in Probability and Statistics, where I heard an interesting talk by Andy Tsao. His talk “Naivity can be good: a theoretical study of naive regression” (Abstract #0586) was about the use of Naive Regression, which is the application of linear regression to a categorical outcome, treating the outcome as numerical. He asserted that predictions from Naive Regression will be quite good. My last post was about the “goodness” of a linear regression applied to a binary outcome in terms of the estimated coefficients. That’s what explanatory modeling is about. What Dr. Tsao alerted me to, … Continue reading Linear regression for binary outcome: even better news