Table 4.

Estimates of the fixed effects from nonlinear quantile mixed-effects regression (NLQMM) and from nonlinear quantile regression (NLRQ) with τ ∊{0.1, 0.5, 0.9} for the fourth scenario. The estimates are averaged over 500 replications and the standard deviations are reported in brackets. Data were generated using the model’s parameter β (2, 0.8, 0.4, −1.5)^⊤.

	β1	β2	β3	β4	PNR
NLQMM (M = 50, n = 5)
τ = 0.1	2.11 (0.63)	1.02 (0.55)	0.30 (0.13)	−∞ (∞)	0.04
τ = 0.5	2.06 (0.28)	0.72 (0.22)	0.88 (0.13)	−3.06 (0.31)	0.50
τ = 0.9	2.09 (0.40)	0.71 (0.31)	1.65 (0.16)	−2.40 (0.18)	0.94
NLRQ(M = 50, n = 5)
τ = 0.1	2.00 (0.60)	0.96 (0.37)	0.46 (0.15)	−∞ (∞)	0.09
τ = 0.5	2.07 (0.38)	0.71 (0.30)	0.87 (0.15)	−3.24 (1.52)	0.50
τ = 0.9	2.24 (0.73)	0.63 (0.42)	1.40 (0.24)	−0.92 (24.07)	0.88
NLQMM (M = 100, n = 5)
τ = 0.1	1.99 (0.26)	0.99 (0.16)	0.37 (0.11)	−∞ (∞)	0.04
τ = 0.5	2.04 (0.19)	0.69 (0.16)	0.94 (0.11)	−3.15 (0.25)	0.50
τ = 0.9	2.05 (0.24)	0.66 (0.19)	1.71 (0.12)	−2.46 (0.14)	0.94
NLRQ(M = 100, n = 5)
τ = 0.1	1.91 (0.37)	0.98 (0.25)	0.54 (0.13)	−∞ (∞)	0.09
τ = 0.5	2.05 (0.23)	0.68 (0.20)	0.93 (0.13)	−3.23 (0.47)	0.50
τ = 0.9	2.17 (0.31)	0.59 (0.27)	1.46 (0.16)	−1.80 (3.46)	0.88
NLQMM (M = 100, n = 10)
τ = 0.1	1.93 (0.19)	1.06 (0.12)	0.46 (0.13)	−∞ (∞)	0.06
τ = 0.5	2.04 (0.15)	0.72 (0.10)	1.03 (0.11)	−3.19 (0.17)	0.50
τ = 0.9	2.05 (0.17)	0.61 (0.15)	1.82 (0.12)	−2.48 (0.11)	0.94
NLRQ(M = 100, n = 10)
τ = 0.1	1.86 (0.26)	0.98 (0.19)	0.61 (0.14)	−∞ (∞)	0.09
τ = 0.5	2.04 (0.18)	0.70 (0.14)	1.01 (0.13)	−3.27 (0.24)	0.50
τ = 0.9	2.17 (0.21)	0.61 (0.19)	1.55 (0.14)	−2.32 (1.89)	0.90