A MINE alternative to D-optimal designs for the linear model
Bouffier, Amanda Marie
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The Maximally Informative Next Experiment (MINE) criterion was developed for designing large, expensive genomics experiments. Four variations of the MINE method for the linear model were created: MINE-like, MINE, MINE with random orthonormal basis, and MINE with rotation for the linear model. Theorem 1 establishes sufficient conditions for the maximization of a MINE criterion under the linear model. Theorem 2 is established when the MINE criterion is equivalent to the classic design criterion of D-optimality. By simulation under the linear model, we establish that the MINE with random orthonormal basis and MINE with random rotation are faster to discover the true linear relation with p regression coefficients and n observations when p >> n. These two variations also display a lower false positive rate than MINE or MINE-like methods for a least a majority of the experiments.