Skip to main content

STICERD Econometrics Seminar Series

New approach to distribution-free testing for Markov chains

Estate Khmaladze (Victoria University of Wellington)

Thursday 26 September 2019 12:30 - 14:00

32L 2.04, 2nd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH

Many of our seminars and public events this year will continue as online seminars or as online and in person. Please check our website listings and Twitter feed @STICERD_LSE for updates.

Unless otherwise specified, current restrictions mean in-person seminars are only open to members of the LSE community (those with a valid LSE ID card).

Those unable to join the seminars in-person are welcome to participate via zoom.


About this event

Consider an empirical process, in any one of statistical contexts, and then apply unitary operator to this processes. Can one say what good could come out of this, and why will it be useful? The answer is that probably one can, as it leads us to a new point of view on distribution-free testing of probabilistic models. The specific answer in the case of parametric families of discrete distributions was described in 2013. For parametric empirical processes in $\R^d$ the approach was described in 2016. In this talk we will show two further examples: how can we have a theory of distribution-free tests for transition matrices of Markov chains, and, if we have time enough, and if my colleagues will find it of interest, how can we test regression model in the way, which does not depend on covariates

STICERD Econometrics seminars are held on Thursdays in term time at 14.00-15.30, ONLINE, unless specified otherwise.

Seminar organisers: Professor Tai Otsu and Dr. Vassilis Hajivassiliou.

For further information please contact Lubala Chibwe, either by email: l.chibwe@lse.ac.uk.

Please use this link to subscribe or unsubscribe to STICERD Econometrics mailing list (emetrics).


This event will take place in 32L 2.04, 2nd Floor Conference Room, LSE, 32 Lincoln's Inn Fields, London WC2A 3PH. The building is labelled on the map.

Campus
LSE Campus Map