Home HomeContact ContactSitemap SitemapPrivate Zone Private ZoneSlovenská verzia Slovenská verzia
Institute of Measurement Science SAS Slovak Academy of Sciences (SAS)
Organization Structure
- - - - - - -
Common Laboratories
- - - - - - -
Selected Results
Publications and Citations
Annual Reports
- - - - - - -
Doctoral Study
Pedagogic Activities
Offered Jobs
Home arrow Seminars arrow Structural Breaks via Regularization Approaches and Tests of Shape Constraints
Structural Breaks via Regularization Approaches and Tests of Shape Constraints


We invite you to the seminar organized by the Department of Theoretical Methods, Institute of Measurement Science SAS where RNDr. Matúš Maciak, Ph.D. from the Department of Probability and Mathematical Statistics, Faculty of Mathematics and Physics, Charles University in Prague, will present an invited talk on Structural Breaks via Regularization Approaches and Tests of Shape Constraints. The seminar will take place on Thursday, May 26, 2016 at 10:00, at the Institute of Measurement Science SAS in Bratislava.


Estimating of various types of structural changes in some unknown dependence structure is an important task in regression modelling approaches especially when dealing with more complex data where sudden changes can be naturally expected. Our estimation method is motivated by some machine learning ideas and using recent developments in statistic, post-selection inference especially, we propose a fully data driven estimation approach where the unknown model and possible change-points are estimated all at once. The estimation approach apriori considers all possible model alternatives which makes the method suitable in situations where no knowledge on the number or position of change-points is given in advance. The estimation approach allows for different structures of change-points to be considered and, as an easy and straightforward extension, I can also incorporate additional shape restrictions which might be useful in some practical scenarios. We also discuss some testing approaches to test such shape restrictions in the regularized model (e.g. testing monotonicity, convexity, etc.).

Measurement Science Review (On-Line Journal)