STATISTICS SEMINAR :: Estimation of Poisson Intensity in the Presence of Deadtime

*********************************
There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
*********************************

Event Details
  • Date/Time:
    • Friday November 17, 2006
      11:00 am - 10:59 pm
  • Location: Executive classroom of Main building of ISyE, GT
  • Phone:
  • URL:
  • Email:
  • Fee(s):
    N/A
  • Extras:
Contact
Barbara Christopher
Industrial and Systems Engineering
Contact Barbara Christopher
404.385.3102
Summaries

Summary Sentence: STATISTICS SEMINAR :: Estimation of Poisson Intensity in the Presence of Deadtime

Full Summary: STATISTICS SEMINAR :: Estimation of Poisson Intensity in the Presence of Deadtime

Occurrence of dead time in recording instruments poses challenging problems in data acquisition and statistical analysis. Well known examples are signals recorded by the Geiger counter and the electron multiplier. In this presentation, we consider statistical analysis of recordings from the Phase Doppler Interferometry (PDI). PDI is a non-intrusive technique for obtaining information about spray characteristics in many areas of science, including liquid fuel spray in combustion, spray coatings, fire suppression and pesticide dispensing. PDI can record the velocity of individual droplets in a spray. But it will miss some of the droplets because of a recurring presence of dead time. The incompleteness of the PDI recordings results in a multimodal interarrival time distribution of droplets. Modeling a spray process as a homogeneous Poisson process, we estimate the spray diffusion rate (Poisson intensity) with a correction for dead time under various conditions. The asymptotic distribution of the estimates is derived from a strict stationary process. Simulation produced a good agreement between our estimates (in the presence of dead time) and the MLE obtained without dead time. Experimental data from NIST are used for illustration.

Additional Information

In Campus Calendar
No
Groups

School of Industrial and Systems Engineering (ISYE)

Invited Audience
No audiences were selected.
Categories
Seminar/Lecture/Colloquium
Keywords
No keywords were submitted.
Status
  • Created By: Barbara Christopher
  • Workflow Status: Published
  • Created On: Oct 8, 2010 - 7:32am
  • Last Updated: Oct 7, 2016 - 9:52pm