Mixed-Effects Logistic Regression Model for Missile Reliability Prediction

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$69,976.00
Award Year:
2004
Program:
SBIR
Phase:
Phase I
Contract:
W31P4Q-04-C-R113
Award Id:
68258
Agency Tracking Number:
A032-2578
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
500 West Cummings Park - Ste 3000, Woburn, MA, 01801
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
859244204
Principal Investigator:
Ssu-HsinYu
Group Leader
(781) 933-5355
syu@ssci.com
Business Contact:
RamanMehra
President
(781) 933-5355
rkm@ssci.com
Research Institute:
n/a
Abstract
In this Phase I SBIR project we propose to use a Linear Mixed Effects Logistic Regression Model for missile reliability assessment. An effective model candidate for missile reliability assessment must be flexible enough to incorporate the different nature of covariates, yet simple enough to allow practical methods for parameter estimation using a realistic amount of sample data. A Logistic Regression Model is particularly suitable for modeling dichotomous data such as pass/fail in missile tests. The main advantage of a Mixed-Effects Model is that it provides a systematic method of aggregating a large numbers of effects of different nature into a manageable number of random effect terms in the model. This in turn allows us to estimate the model parameters efficiently. By combining the desirable features of these two models, we expect the proposed model will perform favorably compared to standard linear regression models. During this Phase I project we will obtain test data, determine the appropriate model structure and estimate the model parameters, and evaluate the model prediction performance to demonstrate the feasibility of the proposed model. We will also investigate the use of the Expectation-Maximization algorithm to cope with the issue of incomplete covariates data. Dr. Alyson Wilson and Dr. Nicholas Hengartner from Los Alamos Lab will provide consulting support on the choice of model structure and covariates, and parameter estimation issues during the Phase I period.

* information listed above is at the time of submission.

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