getFixRobIC             package:ROptEst             R Documentation

_G_e_n_e_r_i_c _F_u_n_c_t_i_o_n _f_o_r _t_h_e _C_o_m_p_u_t_a_t_i_o_n _o_f _O_p_t_i_m_a_l_l_y _R_o_b_u_s_t _I_C_s

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

     Generic function for the computation of optimally robust ICs  in
     case of robust models with fixed neighborhoods. This function is 
     rarely called directly.

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

     getFixRobIC(Distr, risk, neighbor, ...)

     ## S4 method for signature 'Norm, fiUnOvShoot,
     ##   UncondNeighborhood':
     getFixRobIC(Distr, risk, neighbor, 
                 sampleSize, upper, maxiter, tol, warn, Algo, cont)

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

   Distr: object of class '"Distribution"'. 

    risk: object of class '"RiskType"'. 

neighbor: object of class '"Neighborhood"'. 

     ...: additional parameters. 

sampleSize: integer: sample size. 

   upper: upper bound for the optimal clipping bound. 

 maxiter: the maximum number of iterations. 

     tol: the desired accuracy (convergence tolerance).

    warn: logical: print warnings. 

    Algo: "A" or "B". 

    cont: "left" or "right". 

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

     The optimally robust IC is computed.

_M_e_t_h_o_d_s:

     _D_i_s_t_r = "_N_o_r_m", _r_i_s_k = "_f_i_U_n_O_v_S_h_o_o_t", _n_e_i_g_h_b_o_r = "_U_n_c_o_n_d_N_e_i_g_h_b_o_r_h_o_o_d" 
          computes the optimally robust influence curve for
          one-dimensional normal location and finite-sample
          under-/overshoot risk. 

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

     Matthias Kohl Matthias.Kohl@stamats.de

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

     Huber, P.J. (1968) Robust Confidence Limits. Z.
     Wahrscheinlichkeitstheor. Verw. Geb. *10*:269-278.

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

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

     'FixRobModel-class'

