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Table 3 Performance of different weighted measures of dependence reported as \(100 \int |\hat {C}({\mathbf {u}})- C({\mathbf {u}})|\mathrm {d}{\mathbf {u}} / \int |\hat {C}_{\boldsymbol {\lambda }}({\mathbf {u}})-C({\mathbf {u}})|\mathrm {d}{\mathbf {u}}\) or by a ratio of the kind \(100\,\text {MSE}(\hat {\rho })/\text {MSE}(\hat {\rho }_{\boldsymbol {\lambda }})\)

From: Rank correlation under categorical confounding

 

ρ=0.1

ρ=0.5

ρ=0.9

 

n=10

20

50

n=10

20

50

n=10

20

50

\(\hat {C}_{\boldsymbol {\lambda }}\)

100

100

100

100

100

100

100

100

100

\(\hat {C}_{{\boldsymbol {\mu }}}\)

93

93

93

94

95

95

99

99

99

\(\hat {\rho }_{\boldsymbol {\lambda }}\)

93

94

98

78

87

95

35

48

69

\(\hat {\rho }_{{\boldsymbol {\mu }}}\)

60

64

67

53

63

72

33

46

68

\(\hat {\tau }_{\boldsymbol {\lambda }}\)

79

86

95

76

86

95

66

77

91

\(\hat {\tau }_{{\boldsymbol {\mu }}}\)

52

59

65

54

64

72

61

73

88

\(\widetilde {\tau }_{{\boldsymbol {\mu }}}\)

45

56

63

46

59

70

39

50

72

  1. In a practical situation, the confounding would make it impossible to calculate \(\hat {C}\), \(\hat {\rho }\) and \(\hat {\tau }\) on the whole dataset, but they are used here as unattainable ideal benchmarks. Five samples of size n are simulated from a Clayton distribution with Spearman’s correlation ρ. Each scenario is repeated 10,000 times