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Loss-Based Estimation with Evolutionary Algorithms and Cross-Validation

• David Shilane, Division of Biostatistics, School of Public Health, University of California, Berkeley
• Richard H. Liang, Department of Statistics, University of California, Berkeley
• Sandrine Dudoit, Division of Biostatistics, School of Public Health, University of California, Berkeley

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Abstract:

Many statistical inference methods rely upon selection procedures to estimate a parameter of the joint distribution of explanatory and outcome data, such as the regression function. Within the general framework for loss-based estimation of Dudoit and van der Laan, this project proposes an evolutionary algorithm (EA) as a procedure for risk optimization. We also analyze the size of the parameter space for polynomial regression under an interaction constraints along with constraints on either the polynomial or variable degree.

Subject Area:

Computation, General Biostatistics, Statistical Theory and Methods

Suggested Citation:

David Shilane, Richard H. Liang, and Sandrine Dudoit, "Loss-Based Estimation with Evolutionary Algorithms and Cross-Validation" (November 2007). U.C. Berkeley Division of Biostatistics Working Paper Series. Working Paper 227.
http://www.bepress.com/ucbbiostat/paper227