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Table 1 Temporal-longitudinal scenario: comparison between SES equipped with GLMM (SESglmm) and SES equipped with GEE

From: Feature selection for high-dimensional temporal data

Dataset MSPE Average time (in seconds)
  SESglmm SESgee(CS) SESgee(AR(1)) SESglmm SESgee(CS) SESgee(AR(1))
GDS5088 0.131 (0.000) 0.189 (0.1) 0.289 (0.018) 1562.51 (230.53) 1022.45 (217.99) 933.14 (180.34)
GDS4395 0.116 (0.007) 0.156 (0.019) 0.298 (0.028) 21167.21 (26089.48) 4862.15 (1724.89) 5577.80 (1890.15)
GDS4822 0.066 (0.000) 0.055 (0.001) 0.045 (0.004) 1785.66 (321.92) 2103.96 (490.74) 1492.30 (205.03)
GDS3326 0.062 (0.001) 0.052 (0.000) 0.063 (0.002) 6617.09 (472.16) 3167.78 (795.74) 2348.69 (390.10)
GDS3181 0.805 (0.096) 0.458 (0.000) 0.458 (0.00) 1684.90 (206.26) 1011.44 (152.59) 748.18 (105.32)
GDS4258 0.074 (0.000) 0.149 (0.003) 0.152 (0.002) 4135.76 (506.15) 2818.024 (418.97) 2078.52 (462.30)
GDS3915 0.527 (0.038) 0.553 (0.01) 0.439 (0.000) 669.18 (63.93) 511.82 (84.22) 491.91 (108.64)
GDS3432 0.057 (0.001) 0.060 (0.008) 0.038 (0.003) 3275.22 (474.06) 2213.11 (371.68) 2104.05 (546.76)
Average 0.230 (0.280) 0.209 (0.192) 0.223 (0.172) 5112.2 (6756.04) 2378.56 (1566.36) 1971.82 (1611.13)
  1. The latter is indicated as SESgee(CS) and SESgee(AR(1)), depending by the employed variance estimator. TT-corrected, cross-validated mean square prediction error are reported for each dataset, along with their standard deviation (in parenthesis). Average (standard deviation) computational time is reported as well, while the last line reports performances averaged across datasets. The MSPE values are not statistically different, however SESgee(AR(1)) is faster than the other alternatives