MackChainladder.py 文件源码

def __get_tail_weighted_time_period(self):
        """ Method to approximate the weighted-average development age assuming
        exponential tail fit.

        Returns: float32
        """
        #n = self.triangle.ncol-1
        #y = self.f[:n]
        #x = np.array([i + 1 for i in range(len(y))]) 
        #ldf_reg = stats.linregress(x, np.log(y - 1))
        #time_pd = (np.log(self.f[n] - 1) - ldf_reg[1]) / ldf_reg[0]

        n = self.triangle.ncol-1
        y = Series(self.f[:n])
        x = [num+1 for num, item in enumerate(y)]
        y.index = x
        x = sm.add_constant((y.index)[y>1])
        y = y[y>1]
        ldf_reg = sm.OLS(np.log(y-1),x).fit()
        time_pd = (np.log(self.f[n] - 1) - ldf_reg.params[0]) / ldf_reg.params[1]
        #tail_factor = np.exp(tail_model.params[0] + np.array([i+2 for i in range(n,n+100)]) * tail_model.params[1]).astype(float) + 1)

        return time_pd