code: def calc_upper_bca_percentile(alpha_percent, bias_correction, acceleration):
      
      z_upper = norm.ppf(1 - alpha_percent / (100.0 * 2))
      numerator = bias_correction + z_upper
      denominator = 1 - acceleration * numerator
      upper_percentile =\
          norm.cdf(bias_correction + numerator / denominator) * 100
      return upper_percentile
docs: Calculate the lower values of the Bias Corrected and Accelerated (BCa)
      bootstrap confidence intervals.
  
      Parameters
      ----------
      alpha_percent : float in (0.0, 100.0).
          `100 - confidence_percentage`, where `confidence_percentage` is the
          confidence level (such as 95%), expressed as a percent.
      bias_correction : 1D ndarray.
          There will be one element for each element in `mle_estimate`. Elements
          denote the bias correction factors for each component of the parameter
          vector.
      acceleration : 1D ndarray.
          There will be one element for each element in `mle_estimate`. Elements
          denote the acceleration factors for each component of the parameter
          vector.
  
      Returns
      -------
      upper_percentile : 1D ndarray.
          There will be one element for each element in `mle_estimate`. Elements
          denote the larger values in the confidence interval for each component
          of the parameter vector.
  
      References
      ----------
      Efron, Bradley, and Robert J. Tibshirani. An Introduction to the Bootstrap.
          CRC press, 1994. Section 14.3, Equation 14.10.
  
      Notes
      -----
      The `alpha` used in this function is different from the `alpha` used in
      Efron and Tibshirani (1994). The `alpha` used in this function must be
      converted to a decimal (by dividing by 100) and then divided by 2 (to
      account for the equal-tailed nature of the confidence interval) in order to
      be made equivalent to the `alpha` in Efron and Tibshirani (1994).