ROBUST MISSING DATA METHODS FOR CATEGORICAL REGRESSION

Award Information
Agency:
Department of Health and Human Services
Branch
n/a
Amount:
$99,989.00
Award Year:
2002
Program:
SBIR
Phase:
Phase I
Contract:
1R43AA013768-01
Agency Tracking Number:
AA013768
Solicitation Year:
n/a
Solicitation Topic Code:
n/a
Solicitation Number:
n/a
Small Business Information
MARTINGALE RESEARCH CORPORATION
MARTINGALE RESEARCH CORPORATION, 2323 ASHLEY PARK, PLANO, TX, 75074
Hubzone Owned:
N
Minority Owned:
N
Woman Owned:
N
Duns:
n/a
Principal Investigator:
STEVEN HENLEY
(972) 881-8370
STEVENH@MARTINGALE-RESEARCH.COM
Business Contact:
STEVEN HENLEY
(972) 881-8370
STENENH@MARTINGALE-RESEARCH.COM
Research Institution:
n/a
Abstract
DESCRIPTION (provided by applicant): Improved methods for obtaining robust statistical inferences from categorical regression models in the presence of missing data and model misspecification would be an invaluable tool to the epidemiological and health care research communities. Presently epidemiological models are typically designed to identify patterns of alcohol-related symptoms, define criteria of alcohol use disorders, and evaluate policies regulating use and distribution of alcoholic beverages. Such models frequently rely on datasets that contain incomplete-data. While commercially available statistical software provides some automated missing value procedures (e.g., data imputation, Expectation-Maximization), further theoretical and empirical research is required to develop more robust statistical methods. Martingale Research will develop Robust Expectation-Maximization (REM) algorithms that combine recent advances in stochastic estimation, asymptotic statistics, and maximum likelihood recoding to achieve the following objectives: 1) design and implement REM algorithms that are suited to categorical regression modeling for epidemiological problems, 2) theoretically and empirically investigate REM algorithm performance in the presence of missing data and model misspecification and 3) extend the REM algorithms to optimally estimate preprocessing transformations in the presence of missing data and model misspecification. These results will demonstrate the essential technical feasibility required for further Phase II investigations and provide the foundation for developing commercially available software.

* information listed above is at the time of submission.

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