Benchmark Report [Results .csv]

Configuration

Length of signals: 1024

Repetitions: 2000

SNRin values: -5, 0, 5, 10,

Methods

• monte_carlo_test

• global_mad_test

• global_rank_env_test

• APF

Signals

• LinearChirp

Mean results tables:

The results shown here are the average and 95% Clopper-Pearson CI of the estimated detection power with Bonferroni correction. Best performances are bolded.

Signal: LinearChirp [View Plot] [Get .csv]

	Method + Param	SNRin=-5dB (average)	SNRin=-5dB (CI)	SNRin=0dB (average)	SNRin=0dB (CI)	SNRin=5dB (average)	SNRin=5dB (CI)	SNRin=10dB (average)	SNRin=10dB (CI)
0	monte_carlo_test{‘statistic’: ‘Frs’, ‘pnorm’: 2, ‘rmax’: 1.0, ‘MC_reps’: 199}	0.10	[‘0.08’, ‘0.11’]	0.34	[‘0.31’, ‘0.37’]	0.90	[‘0.88’, ‘0.92’]	1.00	[‘1.00’, ‘1.00’]
1	monte_carlo_test{‘statistic’: ‘Frs_vs’, ‘pnorm’: 2, ‘rmax’: 1.0, ‘MC_reps’: 199}	0.12	[‘0.10’, ‘0.14’]	0.65	[‘0.61’, ‘0.67’]	1.00	[‘1.00’, ‘1.00’]	1.00	[‘1.00’, ‘1.00’]
2	global_mad_test{‘statistic’: ‘Frs’, ‘MC_reps’: 199}	0.09	[‘0.07’, ‘0.11’]	0.31	[‘0.28’, ‘0.34’]	0.86	[‘0.84’, ‘0.88’]	1.00	[‘1.00’, ‘1.00’]
3	global_mad_test{‘statistic’: ‘Frs_vs’, ‘MC_reps’: 199}	0.11	[‘0.09’, ‘0.13’]	0.65	[‘0.62’, ‘0.68’]	1.00	[‘1.00’, ‘1.00’]	1.00	[‘1.00’, ‘1.00’]
4	global_rank_env_test{‘fun’: ‘Fest’, ‘correction’: ‘rs’}	0.10	[‘0.08’, ‘0.12’]	0.57	[‘0.54’, ‘0.60’]	1.00	[‘1.00’, ‘1.00’]	1.00	[‘1.00’, ‘1.00’]
5	global_rank_env_test{‘fun’: ‘Fest’, ‘correction’: ‘rs’, ‘rmin’: 0.65, ‘rmax’: 1.05}	0.15	[‘0.12’, ‘0.17’]	0.73	[‘0.71’, ‘0.76’]	1.00	[‘1.00’, ‘1.00’]	1.00	[‘1.00’, ‘1.00’]
6	global_rank_env_test{‘fun’: ‘Fest’, ‘correction’: ‘rs’, ‘transform’: ‘asin(sqrt(.))’}	0.11	[‘0.09’, ‘0.13’]	0.58	[‘0.55’, ‘0.61’]	1.00	[‘1.00’, ‘1.00’]	1.00	[‘1.00’, ‘1.00’]
7	global_rank_env_test{‘fun’: ‘Fest’, ‘correction’: ‘rs’, ‘rmin’: 0.65, ‘rmax’: 1.05, ‘transform’: ‘asin(sqrt(.))’}	0.14	[‘0.12’, ‘0.17’]	0.74	[‘0.71’, ‘0.76’]	1.00	[‘1.00’, ‘1.00’]	1.00	[‘1.00’, ‘1.00’]
8	APF	0.10	[‘0.09’, ‘0.12’]	0.22	[‘0.20’, ‘0.25’]	0.37	[‘0.34’, ‘0.40’]	0.52	[‘0.49’, ‘0.55’]