Neyman Seminar - Nonparametric and adaptive modeling of dynamic seasonality and trend with heteroscedastic and dependent errors
Feb 20, 2013, 04:00 PM - 05:00 PM | 1011 Evans Hall | Happening As Scheduled
Hau-Tieng Wu, Department of Statistics, University of California, Berkeley
Seasonality (or periodicity) and trend are features describing an observed time series, and extracting these features is an important issue in many scientific fields. However, it is not an easy task for existing methods to analyze simultaneously the trend and dynamics of the seasonality, such as time-varying frequency and amplitude; and the adaptivity of the analysis to such dynamics and...