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EPEM: Efficient Parameter Estimation for Multiple Class Monotone Missing Data
- Source :
- Information Sciences. 567:1-22
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- The problem of monotone missing data has been broadly studied during the last two decades and has many applications in various fields such as bioinformatics or statistics. Commonly used imputation techniques require multiple iterations through the data before yielding convergence. Moreover, those approaches may introduce extra noises and biases to the subsequent modeling. In this work, we derive exact formulas and propose a novel algorithm to compute the maximum likelihood estimators (MLEs) of a multiple class, monotone missing dataset when all the covariance matrices of all categories are assumed to be equal, namely Efficient Parameter Estimation for Multiple Class Monotone Missing Data (EPEM). We then illustrate an application of our proposed methods in Linear Discriminant Analysis (LDA). As the computation is exact, our EPEM algorithm does not require multiple iterations through the data as other imputation approaches, thus promising to handle much less time-consuming than other methods. This effectiveness was validated by empirical results when EPEM reduced the error rates significantly and required a short computation time compared to several imputation-based approaches. We also release all codes and data of our experiments in a GitHub repository to contribute to the research community related to this problem.
- Subjects :
- Information Systems and Management
Computer science
Estimation theory
05 social sciences
050301 education
Estimator
02 engineering and technology
Covariance
Linear discriminant analysis
Missing data
Computer Science Applications
Theoretical Computer Science
Monotone polygon
Artificial Intelligence
Control and Systems Engineering
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Imputation (statistics)
0503 education
Algorithm
Software
Subjects
Details
- ISSN :
- 00200255
- Volume :
- 567
- Database :
- OpenAIRE
- Journal :
- Information Sciences
- Accession number :
- edsair.doi...........21c15cc2d61a094add618fc3ad43f7cb