Under the microscope: SemStat Elements
A few months ago, Cambridge University Press launched a new set of succinct, yet information-rich products known as SemStat Elements, edited by Ernst Wit, Chair of Statistics and Probability at the University of Groningen. The perfect marriage between journal articles and books, this series is the exclusive vehicle for short monographs prepared by the invited lecturers at the Seminaires Européens de Statistiques. SemStat is known for high-quality courses of expository lectures on important new developments in statistics delivered at a biennial meeting to an audience of advanced Ph.D. students. Each meeting is organized around a particular theme by the European Regional Committee of the Bernoulli Society, and the Bernoulli Society sponsors the SemStat Elements series.
Areas of interest include:
- Causal Inference
- Model selection and inference
- Bayesian nonparametrics
- Statistics for big data (large p, small n)
- Functional data analysis
- copulas and dependence structures
- Differential geometry of statistical models
- Statistics for differential equations
Two Elements kicked it off, both of which are free to view online for a limited time.
Graphical Models for Categorical Data by Alberto Roverato of the Università di Bologna is a compact account covering both well-established methodology and the theory of models recently introduced in the graphical model literature.
Topics at the Frontier of Statistics and Network Analysis by Boston University’s Eric D. Kolaczyk is a snapshot of the frontier of statistics and network analysis that focuses on the foundational topics of modeling, sampling, and design.
Both of these are also available for purchase in print.