Table 1.

The parameter estimates for the linear regression model for the Hertzsprung–Russell data using several minimum pseudodistance (MP) methods, several minimum density power divergence (MDPD) methods, the least median of squares (LMS) method, S-estimators and the MLE method. $\gamma$ represents tuning parameter.

MLE Estimates
	$\alpha$	$\beta$	$\sigma$
	6.79	−0.41	0.55
MP Estimates
$\gamma$	$\alpha$	$\beta$	$\sigma$
$0.01$	$6.79$	$-0.41$	$0.55$
$0.1$	$6.81$	$-0.41$	$0.56$
$0.25$	$6.86$	$-0.42$	$0.58$
$0.3$	$6.88$	$-0.42$	$0.59$
$0.31$	$6.89$	$-0.43$	$0.59$
$0.32$	$-6.81$	$2.66$	$0.39$
$0.35$	$-7.16$	$2.74$	$0.38$
$0.4$	$-7.62$	$2.85$	$0.38$
$0.5$	$-8.17$	$2.97$	$0.37$
$0.75$	$-8.65$	$3.08$	$0.38$
1	$-8.84$	$3.12$	$0.39$
$1.2$	$-8.94$	$3.15$	$0.40$
$1.5$	$-9.08$	$3.18$	$0.41$
2	$-9.31$	$3.23$	$0.43$
MDPD Estimates
$\gamma$	$\alpha$	$\beta$	$\sigma$
$0.1$	$6.78$	$-0.41$	$0.60$
$0.25$	$-5.16$	$2.30$	$0.42$
$0.5$	$-7.22$	$2.76$	$0.40$
$0.8$	$-7.89$	$2.91$	$0.40$
1	$-8.03$	$2.95$	$0.41$
S-Estimates
	$\alpha$	$\beta$	$\sigma$
	$-9.59$	$3.28$	−
LMS Estimates
	$\alpha$	$\beta$	$\sigma$
	$-12.30$	$3.90$	−