def huber(x):
res = sm.RLM(x, np.ones(len(x)),
M=norms.HuberT()).fit(scale_est=scale.HuberScale())
return res.params[0]
# m1_Bisquare = RLM(data.endog, data.exog, M=norms.TukeyBiweight())
# results_Bisquare1 = m1_Bisquare.fit()
# m2_Bisquare = RLM(data.endog, data.exog, M=norms.TukeyBiweight())
# results_Bisquare2 = m2_Bisquare.fit(cov="H2")
# m3_Bisquare = RLM(data.endog, data.exog, M=norms.TukeyBiweight())
# results_Bisquare3 = m3_Bisquare.fit(cov="H3")
##############################################
# Huber's Proposal 2 scaling #
##############################################
################
### Huber'sT ###
################
m1_Huber_H = RLM(data.endog, data.exog, M=norms.HuberT())
results_Huber1_H = m1_Huber_H.fit(scale_est=scale.HuberScale())
# m2_Huber_H
# m3_Huber_H
# m4 = RLM(data.endog, data.exog, M=norms.HuberT())
# results4 = m1.fit(scale_est="Huber")
# m5 = RLM(data.endog, data.exog, M=norms.Hampel())
# results5 = m2.fit(scale_est="Huber")
# m6 = RLM(data.endog, data.exog, M=norms.TukeyBiweight())
# results6 = m3.fit(scale_est="Huber")
# print """Least squares fit
#%s
#Huber Params, t = 2.
#%s
#Ramsay's E Params
#%s
