class_docstr: Quantile Regression
  
  Estimate a quantile regression model using iterative reweighted least
  squares.
  
  Parameters
  ----------
  endog : array or dataframe
      endogenous/response variable
  exog : array or dataframe
      exogenous/explanatory variable(s)
  
  Notes
  -----
  The Least Absolute Deviation (LAD) estimator is a special case where
  quantile is set to 0.5 (q argument of the fit method).
  
  The asymptotic covariance matrix is estimated following the procedure in
  Greene (2008, p.407-408), using either the logistic or gaussian kernels
  (kernel argument of the fit method).
  
  References
  ----------
  General:
  
  * Birkes, D. and Y. Dodge(1993). Alternative Methods of Regression, John Wiley and Sons.
  * Green,W. H. (2008). Econometric Analysis. Sixth Edition. International Student Edition.
  * Koenker, R. (2005). Quantile Regression. New York: Cambridge University Press.
  * LeSage, J. P.(1999). Applied Econometrics Using MATLAB,
  
  Kernels (used by the fit method):
  
  * Green (2008) Table 14.2
  
  Bandwidth selection (used by the fit method):
  
  * Bofinger, E. (1975). Estimation of a density function using order statistics. Australian Journal of Statistics 17: 1-17.
  * Chamberlain, G. (1994). Quantile regression, censoring, and the structure of wages. In Advances in Econometrics, Vol. 1: Sixth World Congress, ed. C. A. Sims, 171-209. Cambridge: Cambridge University Press.
  * Hall, P., and S. Sheather. (1988). On the distribution of the Studentized quantile. Journal of the Royal Statistical Society, Series B 50: 381-391.
  
  Keywords: Least Absolute Deviation(LAD) Regression, Quantile Regression,
  Regression, Robust Estimation.
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code: def __init__(self, endog, exog, **kwargs):
      self._check_kwargs(kwargs)
      super().__init__(endog, exog, **kwargs)
  
imports: import numpy
  import warnings
  import scipy.stats
  from numpy.linalg import pinv
  from scipy.stats import norm
  from statsmodels.tools.decorators import cache_readonly
  from statsmodels.regression.linear_model import RegressionModel, RegressionResults, RegressionResultsWrapper
  from statsmodels.tools.sm_exceptions import ConvergenceWarning, IterationLimitWarning
label_desc: Use image node_id 1 to create a new QuantReg object from inherited base classes: RegressionModel with example: obj = QuantReg(endog, exog)