UNIVERSITY OF COLORADO AT DENVER

PLACE: Mathematics Conference Room 626 UCD Building, 1250 14th St., Denver

TIME: NOON (Refreshments served at 11:45 am)

DATE: October 4, 1999

Title
Computational Aspects of Estimation and Prediciton in Nonlinear
State-Space Models when Observations are Subject to Detection Limits


Abstract
State-space models involving time varying parameters are often
used for describing broad classes of biological and physical phenomena. In
some cases, measurement devices that produce data suitable for such models
are hampered by an inability to measure beyond certain specified upper or
lower detection limits. Traditional approaches to estimation for nonlinear
state-space models use maximum likelihood procedures.  These procedures
depend on being able to compute conditional expectations via Kalman
filtering and smoothing which become intractable under censoring or when
using nonlinear models.

Carlin, Poulson and Stoffer (1992)  develop a Markov Chain Monte Carlo
(MCMC) estimation procedure for nonlinear state-space models. This
MCMC method is  extended to fit linear and non-linear state-space
models when observations have been censored due to detection limits. 


These MCMC estimation procedures are applied to filtering and parameter
estimation for nonlinear state-space models of spatio-temporal data
collected by a laser detector (lidar) measuring airborne particulate
matter created by a moving point source where the data collection
device was constrained by an upper detection limit.  

The optimization aspects of the procedure will be highlighted.

-Craig Johns
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Assistant Professor             Visiting Scientist
Mathematics Department          Geophysical Statistics Project
Colorado University, Denver     National Center for Atmospheric Research
cjohns@math.cudenver.edu        cjohns@ucar.edu
(303) 556-2618                  (303) 497-1376   
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