STATISTICS SEMINAR :: Self Consistency: A General Recipe for Wavelet Estimation With Irregularly-Spaced and/or Incomplete Data

GUEST LECTURER
Professor Xiao-Li Meng

AFFILIATION
Department of Statistics, Harvard University

ABSTRACT
We propose a general methodology for conducting wavelet estimation with irregularly-spaced data by viewing them as the observed portion of an augmented regularly-spaced data set. We then invoke the self-consistency principle to define our wavelet estimators in the presence of incomplete data. Major advantages of this approach include: (i) it can be coupled with almost any wavelet shrinkage methods, (ii) it can deal with non--Gaussian or correlated noise, and (iii) it can automatically handle other kinds of missing or incomplete observations. We also develop a multiple-imputation algorithm and fast EM-type algorithms for computing or approximating such estimates. Results from numerical experiments suggest that our algorithms produce favorite results when comparing to several common methods, and therefore we hope these empirical findings would motivate subsequent theoretical investigations. To illustrate the flexibility of our approach, examples with Poisson data smoothing and image denoising are also provided. (This is joint work with Thomas Lee of Colorado State University.)

DATE & TIME
Friday, December 01, 2006 -- 12:00 PM

DURATION
1 hour

LOCATION
Executive classroom of Main building of ISyE, GT

CONTACT PERSON
Yajun Mei