Maximum Likelihood Estimation of the Binary Coefficient of Determination Conference Paper

abstract

• The binary Coefficient of Determination (CoD) is a key component of inference methods in Genomic Signal Processing. Assuming a stochastic logic model, we introduce a new sample CoD estimator based upon maximum likelihood (ML) estimation. Experiments have been conducted to assess how the ML CoD estimator performs in recovering predictors in multivariate prediction settings. Performance is compared with the traditional nonparametric CoD estimators based on resubstitution, leave-one-out, bootstrap and cross-validation. The results show that the ML CoD estimator is the estimator of choice if prior knowledge is available about the logic relationships in the model, even if this knowledge is incomplete. 2011 IEEE.

name of conference

• 2011 Conference Record of the Forty Fifth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)

authors

• Braga Neto, Ulisses

published proceedings

• 2011 CONFERENCE RECORD OF THE FORTY-FIFTH ASILOMAR CONFERENCE ON SIGNALS, SYSTEMS & COMPUTERS (ASILOMAR)

author list (cited authors)

• Chen, T., & Braga-Neto, U.

citation count

• 3

complete list of authors

• Chen, Ting||Braga-Neto, Ulisses

publication date

• January 2011

publisher

• Institute of Electrical and Electronics Engineers (IEEE)  Publisher

published in

• Conference record / Asilomar Conference on Signals, Systems & Computers. Asilomar Conference on Signals, Systems & Computers  Journal

keywords

• Cod Estimation
• Maximum Liklihood Estimation
• Multivariate Predictive Inference
• Stochastic Logic Model

Digital Object Identifier (DOI)

• 10.1109/acssc.2011.6190164

International Standard Book Number (ISBN) 13

• 9781467303231

start page

• 1012

end page

• 1016

URL

• http%3A%2F%2Fdx.doi.org%2F10.1109%2Facssc.2011.6190164