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Table 1 Mean squared prediction error (MSPE) for the LASSO, Bayesian LASSO (BLASSO), genomic BLUP (GBLUP), reproducing kernel Hilbert space (RKHS) regression, random forests (RF) and Bayesian additive regression trees (BART) methods evaluated on the simulated original QTLMAS2010 data

From: Genome-wide prediction using Bayesian additive regression trees

Method Mean squared prediction error (MSPE)
LASSO
 minMSE 62.020
 minMSE + 1SE 63.404
BLASSO 66.209
GBLUP 66.949
RKHS  
 \(h = 0.05\) 66.910
 \(h = 0.1\) 66.821
 \(h = 0.25\) 67.200
RF M = 10 M = 25 M = 50 M = 100 M = 200 M = 400 M = 600
  82.108 79.772 77.794 77.274 77.149 76.141 76.419
BART M = 10 M = 25 M = 50 M = 100 M = 200 M = 400 M = 600
 \(q = 0.9\)
 \(\kappa\) = 2 76.231 69.974 65.703 64.967 64.324 64.213 64.574
 \(\kappa\) = 3 71.325 68.537 66.755 63.772 62.782 62.919 63.476
 \(\kappa\) = 4 79.264 66.554 66.376 63.596 62.595 63.119 63.790
 \(\kappa\) = 5 72.344 70.608 65.467 62.705 62.715 63.997 64.982
 \(q = 0.95\)
 \(\kappa\) = 2 78.656 76.734 68.282 64.126 64.218 63.697 64.566
 \(\kappa\) = 3 74.893 68.379 64.858 63.762 62.884 63.108 63.402
 \(\kappa\) = 4 74.128 66.817 64.788 63.836 62.596 63.175 63.807
 \(\kappa\) = 5 76.757 66.284 64.512 62.648 62.823 63.912 64.976
  1. The lowest MSPE obtained with each method is highlighted in italics. M is the number of trees for RF and BART, and \(q\) and \(\kappa\) are hyperparameters of the BART priors. The stopping criteria for the regularization coefficient λ in LASSO were obtained based on tenfold cross-validation both at minimum MSE and minimum MSE plus 1 standard error [42]