MackChainladder.py 文件源码

def __get_tail_sigma(self):
        """ Method to produce the sigma of the Mack Chainladder 
        model tail factor


        Returns:
            This calculation is consistent with the R calculation 
            MackChainLadder$tail.sigma
        """
        y = np.log(self.sigma[:len(self.triangle.data.columns[:-2])])
        x = np.array([i + 1 for i in range(len(y))])
        model = stats.linregress(x, y)
        tailsigma = np.exp((x[-1] + 1) * model[0] + model[1])
        if model[3] > 0.05:  # p-vale of slope parameter
            y = self.sigma
            tailsigma = np.sqrt(
                abs(min((y[-1]**4 / y[-2]**2), min(y[-2]**2, y[-1]**2))))
        if self.chainladder.tail == True: 
            time_pd = self.__get_tail_weighted_time_period()
            y = np.log(np.append(self.sigma,tailsigma))
            x = np.array([i + 1 for i in range(len(y))])
            sigma_reg = stats.linregress(x, y)
            tailsigma = np.append(tailsigma, np.exp(time_pd * sigma_reg[0] + sigma_reg[1]))
        else:
            tailsigma = np.append(tailsigma,0)
        return tailsigma