Paper of the Month: February 2022

Once a month during the academic year, the statistics faculty select a paper for our students to read and discuss. Papers are selected based on their impact or historical value, or because they contain useful techniques or results.

Nozer D. Singpurwalla, Nicholas G. Polson & Refik Soyer (2018), From Least Squares to Signal Processing and Particle Filtering, Technometrics, 60:2, 146-160.

Notes preparer: Nalini Ravishanker

“Signal processing is the interpolation and extrapolation of a sequence of observations viewed as a realization of a stochastic process. Its role in applied statistics ranges from scenarios in forecasting and time series analysis to image reconstruction, machine learning, and the degradation modeling for reliability assessment.” The Kalman filter algorithm is useful for Gaussian state space models (dynamic linear models), while particle filtering is useful for “big and high velocity non-Gaussian data”. The principle of conditionalization underlies filtering and prediction based on Bayesian methods.

We will follow the path laid out in this paper and discuss ideas for analyzing time course data.


  • Arulampalam, M. S., Maskell, S., Gordon, N., and Clapp, T. (2002), “A Tutorial on Particle Filters for Online Nonlinear/Non-Gaussian Bayesian Tracking,” IEEE Transactions on Signal Processing, 50, 174–188.
  • Liu, J. S., and Chen, R. (1998), “Sequential Monte Carlo Methods for Dynamic Systems,” em>Journal of the American Statistical Association, 93, 1032-1044.
  • Meinhold, R. J., and Singpurwalla, N. D. (1983), “Understanding the Kalman Filter,” The American Statistician, 37, 123-127.