alexa Building Cost-efficient Models using BLARS Method
ISSN: 2155-6180

Journal of Biometrics & Biostatistics
Open Access

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Research Article

Building Cost-efficient Models using BLARS Method

Li Hua Yue*, Wenqing He, Duncan Murdoch and Hristo Sendov

Department of Statistical and Actuarial Sciences, Western University, London, ON N6A5B7, Canada

*Corresponding Author:
Li Hua Yue
Department of Statistical and Actuarial Sciences
Western University, London, ON N6A5B7, Canada
Tel: 1-519-661-2111; ext (86385)
Fax: 1-519-661-3813
E-mail: [email protected]

Received Date: October 15, 2013; Accepted Date: November 07, 2013; Published Date: November 13, 2013

Citation: Yue LH, He W, Murdoch D, Sendov H (2013) Building Cost-efficient Models using BLARS Method. J Biomet Biostat 4: 177. doi: 10.4172/2155-6180.1000177

Copyright: © 2013 Yue LH, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Variable selection is a difficult problem in building statistical models. Identification of cost efficient diagnostic factors is very important to health researchers, but most variable selection methods do not take into account the cost of collecting data for the predictors. The trade-off between statistical significance and cost of collecting data for a statistical model is our focus. In this paper, we extend the LARS variable selection method to incorporate costs of factors in variable selection, which also works with other methods of variable selection, such as Lasso and adaptive Lasso. A branch and bound search method combined with LARS is employed to select cost-efficient factors. We apply the resulting branching LARS method to a dataset from an Assertive Community Treatment project conducted in Southwestern Ontario to demonstrate the cost-efficient variable selection process, and the results show that a “cheaper” model could be selected by sacrificing a user selected amount of model accuracy.

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