JSM 2011 Online Program

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

Abstract Details

Activity Number: 63
Type: Topic Contributed
Date/Time: Sunday, July 31, 2011 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistical Learning and Data Mining
Abstract - #302975
Title: A Frequency Domain EM Algorithm to Detect Similar Dynamics in Time Series with Applications to Spike Sorting and Macro-Economics
Author(s): Georg M. Goerg*+
Companies: Carnegie Mellon University
Address: 5000 Forbes Avenue, Pittsburgh, PA, 15213,
Keywords: statistical learning

In this work I propose a frequency domain adaptation of the Expectation Maximization (EM) algorithm to separate a family of sequential observations in classes of similar dynamic structure, which can either mean non-stationary signals of similar shape, or stationary signals with similar auto-covariance function. It does this by viewing the magnitude of the discrete Fourier transform (DFT) of the signals (or power spectrum) as a probability density/mass function (pdf/pmf) on the unit circle: signals with similar dynamics have similar pdfs; distinct patterns have distinct pdfs. An advantage of this approach is that it does not rely on any parametric form of the dynamic structure, but can be used for non-parametric, robust and model-free classification. Applications to neural spike sorting (non-stationary) and pattern-recognition in socio-economic time series (stationary) demonstrate the usefulness and wide applicability of the proposed method.

The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.

Back to the full JSM 2011 program

2011 JSM Online Program Home

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

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