Portfolio by the numbers
Offering books and journals on collecting, analysing, displaying and interpreting quantitative and qualitative data, we recognise the key role that Statistics plays in a variety of scientific discipline. We aim to promote its importance through a proliferation of resources, and partnerships with societies such as the American Statistical Association for the benefit of researchers, students and professionals.
TAS contains timely articles organized into the following sections: Statistical Practice, General, Teacher's Corner, Statistical Computing and Graphics, Reviews of Books and Teaching Materials, Letters to the Editor, History Corner, and Interdisciplinary.
Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement are reviewed in JASA .
The Journal of Business and Economic Statistics ( JBES ) publishes a range of articles, primarily applied statistical analyses of microeconomic, macroeconomic, forecasting, business, and finance related topics. More general papers in statistics, econometrics, computation, simulation, or graphics are also appropriate if they are immediately applicable to the journal's general topics of interest.
Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds your knowledge of and confidence in making inferences from data. Reflecting the need for scripting in today's model-based statistics, the book pushes you to perform step-by-step calculations that are usually automated.
A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data.
From a review of the first edition: "Modern Data Science with R… is rich with examples and is guided by a strong narrative voice. What’s more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics" (The American Statistician).