BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//CERN//INDICO//EN
BEGIN:VEVENT
SUMMARY:The regression models with dependent errors
DTSTART;VALUE=DATE-TIME:20191218T093000Z
DTEND;VALUE=DATE-TIME:20191218T103000Z
DTSTAMP;VALUE=DATE-TIME:20200926T113426Z
UID:indico-event-5292@indico.math.cnrs.fr
DESCRIPTION:We consider the usual linear regression model in the case wher
e the error process is assumed strictly stationary. We use a result from H
annan (1973)\, who proved a Central Limit Theorem for the usual least squa
res estimator under general conditions on the design and on the error proc
ess. Whatever the design satisfying Hannanâ€™s conditions\, we define an e
stimator of the covariance matrix and we prove its consistency under very
mild conditions. As an application\, we show how to modify the usual tests
on the linear model in this dependent context\, in such a way that the ty
pe-I error rate remains asymptotically correct.\nThen\, we present some re
sults on the non-parametric regression model in the case where the error p
rocess is a Gaussian stationary sequence.\n\nhttps://indico.math.cnrs.fr/e
vent/5292/
LOCATION:
URL:https://indico.math.cnrs.fr/event/5292/
END:VEVENT
END:VCALENDAR