. 2017 Dec 19;10:39. doi: 10.1186/s13040-017-0159-z

Table 2.

Performance of different GLM methods for Poisson regression over 100 simulations, where values in the parenthesis are the standard deviations, and ANSF: Average number of selected features; rMSE: Average square root of mean squared error; $| \hat{β} - β | = \sum_{i} | {\hat{β}}_{i} - β_{i} |$ : average absolute bias when comparing true and estimated parameters

	PMS	glmnet	SparseReg		L ₀ADRIDGE
		L ₁	SCAD	MC+
	rMSE	1.10(±.091)	1.090(±.092)	1 . 0 8 7 ( ± . 0 9 1 )	1.937(±.222)
N =100	$\| \hat{β} - β \|$	1.755(±.274)	1.754(±.275)	1.737±.273)	0 . 2 2 2 ( ± . 1 1 6 )
P =100	ANSF	43.03(±3.52)	43.07(±3.57)	42.06(±3.51)	3 . 9 9 ( ± . 1 0 0 )
	PTM	0%	0%	0%	9 9 %
	FDR	90.6%	90.6%	90.6%	0 %
	rMSE	0.503(±.017)	0.502(±.017)	0 . 5 0 1 ( ± . 0 1 8 )	2.108(±.359)
N =100	$\| \hat{β} - β \|$	2.671(±.421)	2.673(±.425)	2.821±2.012)	0 . 4 2 4 ( ± . 3 5 0 )
P=10³	ANSF	75.47(±5.61)	75.82(±5.71)	75.14(±8.69)	3 . 6 1 0 ( ± . 6 0 1 )
	PTM	0%	0%	0%	6 4 %
	FDR	94.7%	94.7%	94.6%	2 . 4 %
	rMSE	0 . 2 7 1 ( ± . 0 0 4 )	0.272(±.012)	0.275(±.025)	1.916(±.081)
N =500	$\| \hat{β} - β \|$	5.845(±.280)	6.185(±2.359)	5.807±.273)	0 . 0 8 6 ( ± . 0 3 3 )
P=10⁴	ANSF	465.6(±14.1)	475.1(±15.5)	463.6(±13.9)	4 . 0 0 0 ( ± . 0 0 0 )
	PTM	0%	0%	0%	1 0 0 %
	FDR	99.1%	99.2%	99.1%	0 %

PMS: Performance Measures. PTM: Percentage of true models. FDR: False discovery rate. The values in boldface indicate the best performance