rlOptIC                package:RobLox                R Documentation

_C_o_m_p_u_t_a_t_i_o_n _o_f _t_h_e _o_p_t_i_m_a_l_l_y _r_o_b_u_s_t _I_C _f_o_r _A_L _e_s_t_i_m_a_t_o_r_s

_D_e_s_c_r_i_p_t_i_o_n:

     The function 'rlOptIC' computes the optimally robust IC for  AL
     estimators in case of normal location and (convex) contamination 
     neighborhoods. The definition of these estimators can be found  in
     Rieder (1994) or Kohl (2005), respectively.

_U_s_a_g_e:

     rlOptIC(r, mean = 0, sd = 1, bUp = 1000, computeIC = TRUE)

_A_r_g_u_m_e_n_t_s:

       r: non-negative real: neighborhood radius. 

    mean: specified mean.

      sd: specified standard deviation.

     bUp: positive real: the upper end point of the  interval to be
          searched for the clipping bound b. 

computeIC: logical: should IC be computed. See details below. 

_D_e_t_a_i_l_s:

     If 'computeIC' is 'FALSE' only the Lagrange multipliers 'A', 'a',
     and 'b' contained in the optimally robust IC are computed.

_V_a_l_u_e:

     If 'computeIC' is 'TRUE' an object of class '"ContIC"' is
     returned,  otherwise a list of Lagrane multipliers 

       A: standardizing constant 

       a: centering constant; always '= 0' is this symmetric setup 

       b: optimal clipping bound 

_A_u_t_h_o_r(_s):

     Matthias Kohl Matthias.Kohl@stamats.de

_R_e_f_e_r_e_n_c_e_s:

     Rieder, H. (1994) _Robust Asymptotic Statistics_. New York:
     Springer.

     Kohl, M. (2005) _Numerical Contributions to the Asymptotic Theory
     of Robustness_.  Bayreuth: Dissertation.

_S_e_e _A_l_s_o:

     'ContIC-class', 'roblox'

_E_x_a_m_p_l_e_s:

     IC1 <- rlOptIC(r = 0.1)
     distrExOptions("ErelativeTolerance" = 1e-12)
     checkIC(IC1)
     distrExOptions("ErelativeTolerance" = .Machine$double.eps^0.25) # default
     Risks(IC1)
     cent(IC1)
     clip(IC1)
     stand(IC1)
     plot(IC1)

