Yuehua Wu, York University, North York Mestimation and Its Recursive Analog In this talk, some Mestimation based procedures are reviewed. It is noted that many Mestimates have no explicit expressions. Often the Newton approach could not be applied to the computation of Mestimates. Even when the Newton approach is applicable, it is usually selective for the initial values. Thus, when a new observation is obtained, it is onerous and gaucherie to recalculate the estimates from enlarged data set. Hence, there is a need to construct a convenient algorithm up-dating the Mestimates when new observations are consecutively obtained and the estimates need to update from time to time. Some up-dating recursion formulas of Mestimation will be presented in this talk. About the Speaker: Dr. Wu is currently a professor in the Department of Mathematics and Statistics at York University. She obtained her Ph.D degree in Statistics from the University of Pittsburgh in 1989. Her diverse research interests include Mestimation, robust statistics, asymptotic theory, multivariate analysis, model selection, change-point analysis, statistical computing, statistical modelling, survival analysis, statistical signal processing. |
