Asymptotic Methods in Statistics

Polynomial regression under a mixture of classical and Berkson errors with unknown moments of the latter error

Name: Polynomial regression under a mixture of classical and Berkson errors with unknown moments of the latter error
Start: 2026-09-25T17:00:00+03:00
End: 2026-09-25T18:00:00+03:00
Location: ONLINE

by Oleksandr Liubimov (Taras Shevchenko National University of Kyiv and EPFL, Lausanne, Switzerland)

Friday 25 Sept 2026, 17:00 → 18:00 Europe/Kiev

https://knu-ua.zoom.us/j/89643295643?pwd=eTBZZSt0d0thZzFyaUhDUFNGTVE3QT09 (ONLINE)

https://knu-ua.zoom.us/j/89643295643?pwd=eTBZZSt0d0thZzFyaUhDUFNGTVE3QT09

ONLINE

Description

Previously we gave consistent estimators for model parameters given the first 2d moments of Berkson error, where d is the power of the polynomial. Now we weaken this assumption and construct new estimators for the model parameters. In particular we estimate consistently the regression coefficients without information on moments of Berkson error.