class_docstr: Sliced Average Variance Estimation (SAVE)
  
  Parameters
  ----------
  endog : array_like (1d)
      The dependent variable
  exog : array_like (2d)
      The covariates
  bc : bool, optional
      If True, use the bias-corrected CSAVE method of Li and Zhu.
  
  References
  ----------
  RD Cook.  SAVE: A method for dimension reduction and graphics
  in regression.
  http://www.stat.umn.edu/RegGraph/RecentDev/save.pdf
  
  Y Li, L-X Zhu (2007). Asymptotics for sliced average
  variance estimation.  The Annals of Statistics.
  https://arxiv.org/pdf/0708.0462.pdf
class_docstr_tok: ["Sliced","Average","Variance","Estimation","(","SAVE",")","Parameters","--","--","--","--","--","endog",":","array_like","(","1d",")","The","dependent","variable","exog",":","array_like","(","2d",")","The","covariates","bc",":","bool",",","optional","If","True",",","use","the","bias-corrected","CSAVE","method","of","Li","and","Zhu",".","References","--","--","--","--","--","RD","Cook",".","SAVE",":","A","method","for","dimension","reduction","and","graphics","in","regression",".","http",":","\/\/www.stat.umn.edu\/RegGraph\/RecentDev\/save.pdf","Y","Li",",","L-X","Zhu","(","2007",")",".","Asymptotics","for","sliced","average","variance","estimation",".","The","Annals","of","Statistics",".","https",":","\/\/arxiv.org\/pdf\/0708.0462.pdf"]
code: def __init__(self, endog, exog, **kwargs):
      super(SAVE, self).__init__(endog, exog, **kwargs)
  
      self.bc = False
      if "bc" in kwargs and kwargs["bc"] is True:
          self.bc = True
  
imports: import warnings
  import numpy
  import pandas
  from statsmodels.base import model
  import statsmodels.base.wrapper
  from statsmodels.tools.sm_exceptions import ConvergenceWarning
label_desc: Use image node_id 1 to create a new SlicedAverageVarianceEstimation object from inherited base classes: _DimReductionRegression with example: obj = SlicedAverageVarianceEstimation(endog, exog)