QuantReg.fit()

statsmodels.regression.quantile_regression.QuantReg.fit

QuantReg.fit(q=0.5, vcov='robust', kernel='epa', bandwidth='hsheather', max_iter=1000, p_tol=1e-06, **kwargs) [source]

Solve by Iterative Weighted Least Squares

Parameters:

Parameters:	q : float Quantile must be between 0 and 1 vcov : string, method used to calculate the variance-covariance matrix of the parameters. Default is `robust`: robust : heteroskedasticity robust standard errors (as suggested in Greene 6th edition) iid : iid errors (as in Stata 12) kernel : string, kernel to use in the kernel density estimation for the asymptotic covariance matrix: epa: Epanechnikov cos: Cosine gau: Gaussian par: Parzene bandwidth: string, Bandwidth selection method in kernel density estimation for asymptotic covariance estimate (full references in QuantReg docstring): hsheather: Hall-Sheather (1988) bofinger: Bofinger (1975) chamberlain: Chamberlain (1994)

q : float

Quantile must be between 0 and 1

vcov : string, method used to calculate the variance-covariance matrix

of the parameters. Default is robust:

robust : heteroskedasticity robust standard errors (as suggested in Greene 6th edition)
iid : iid errors (as in Stata 12)

kernel : string, kernel to use in the kernel density estimation for the

asymptotic covariance matrix:

bandwidth: string, Bandwidth selection method in kernel density

estimation for asymptotic covariance estimate (full references in QuantReg docstring):

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© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.regression.quantile_regression.QuantReg.fit.html