JSM 2014 Home
Online Program Home
My Program

Abstract Details

Activity Number: 175
Type: Contributed
Date/Time: Monday, August 4, 2014 : 10:30 AM to 12:20 PM
Sponsor: Section on Physical and Engineering Sciences
Abstract #311222
Title: Inference from Short Atmospheric Time Series
Author(s): Alexander Gluhovsky*+
Companies: Purdue University
Keywords: Time series ; dynamical systems ; subsampling
Abstract:

Atmospheric time series analysis presents serious challenges since standard methods entail strong assumptions rarely met in real data, whereas resampling methods, which could provide asymptotically correct inference without having to rely on questionable assumptions, may underperform as observed series are often prohibitively short. For both, evaluations of statistical techniques using Monte Carlo simulations are problematic, since fitted traditional time series models fail to follow the underlying nonlinear data generating mechanism. The talk will discuss handling short observed records by means of novel time series models derived from the governing equations of atmospheric dynamics.


Authors who are presenting talks have a * after their name.

Back to the full JSM 2014 program




2014 JSM Online Program Home

For information, contact jsm@amstat.org or phone (888) 231-3473.

If you have questions about the Professional Development program, please contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

ASA Meetings Department  •  732 North Washington Street, Alexandria, VA 22314  •  (703) 684-1221  •  meetings@amstat.org
Copyright © American Statistical Association.