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Dissertation Proposal
September 20 (Fri) 11 a.m.
Alavi Commons Room, 6625 Everett Tower
Jaime Burgos
Department of Statistics
Western Michigan University
Multivariate time series analysis has become increasingly popular over the past few decades for the task of forecasting. The vector autoregresive model is typically used across different fields due to its simplicity on application. The traditional method for estimating model parameters is least squares due to the linear nature of the model and its similarity with linear regression. However, since least squares estimates are sensitive to outliers, more robust techniques are often of interest.
This paper investigates Schweppe-type weights for a class of weighted Wilcoxon estimates. We conduct a Monte Carlo study to compare performance of several estimation methods. We also apply these various methods to a quadrivariate financial time series.
All statistics students are expected to attend.