. Author manuscript; available in PMC: 2017 May 10.

Published in final edited form as: Stat Med. 2015 Nov 22;35(10):1676–1688. doi: 10.1002/sim.6812

Table 2.

Simulation study when Y given X* was linear and X was mixture-normal

		n = 500			n = 1000
		Naive	NRC	LRC	Naive	NRC	LRC
σ_u = 0.5
β₀	Bias	0.096	−0.019	−0.047	0.098	−0.017	−0.044
	SD	0.066	0.075	0.078	0.047	0.052	0.056
	ASE	0.065	0.072	0.077	0.046	0.051	0.054
	CP	0.688	0.930	0.896	0.402	0.918	0.872
β₁	Bias	−0.213	0.018	0.071	−0.217	0.013	0.066
	SD	0.094	0.114	0.128	0.066	0.078	0.090
	ASE	0.094	0.111	0.127	0.066	0.079	0.090
	CP	0.400	0.936	0.910	0.102	0.956	0.884

		Naive	NRC	LRC	Naive	NRC	LRC
σ_u = 1.0
β₀	Bias	0.180	−0.050	0.014	0.181	−0.049	0.012
	SD	0.071	0.096	0.101	0.046	0.063	0.064
	ASE	0.067	0.089	0.094	0.047	0.063	0.066
	CP	0.242	0.894	0.934	0.038	0.884	0.958
β₁	Bias	−0.409	0.046	−0.023	−0.404	0.053	−0.015
	SD	0.098	0.155	0.166	0.067	0.107	0.112
	ASE	0.096	0.151	0.161	0.068	0.107	0.114
	CP	0.006	0.930	0.926	0.000	0.928	0.962

NOTE: The naive estimator replaced X* by W*, NRC was the normality-based RC estimator, and LRC was the linearity-based RC estimator. The regression parameters were β = (0.5,1.0). The nuisance parameters were μ_x = 0, σ_x = 1. The results were obtained from 500 replicates.