postmed             package:ebayesthresh             R Documentation

_P_o_s_t_e_r_i_o_r _m_e_d_i_a_n _e_s_t_i_m_a_t_o_r

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

     Given a data value or a vector of data, find the corresponding
     posterior median estimate(s) of the underlying signal value(s)

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

     postmed(x, w, prior = "laplace", a = 0.5)

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

       x: a data value or a vector of data

       w: the value of the prior probability that the signal is nonzero 

   prior: family of the nonzero part of the prior; can be "cauchy" or 
          "laplace" 

       a: the scale parameter of the nonzero part of the prior if the
          Laplace prior is used

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

     If x is a scalar, the posterior median med(theta|x) where theta 
     is the mean of the distribution from which x is drawn.     If x is
     a vector with elements x_1, ... , x_n, then the vector returned
     has elements  med(theta_i|x_i), where each x_i has mean theta_i, 
     all with the given prior.

_N_o_t_e:

     If the quasicauchy prior is used, the argument 'a' is ignored. The
     routine calls the approprate one of 'postmed.laplace' or
     'postmed.cauchy'.

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

     Bernard Silverman

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

     See 'ebayesthresh' and <URL: http://www.bernardsilverman.com>

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

     'postmed.laplace', 'postmed.cauchy', 'postmean'

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

     postmed(c(-2,1,0,-4,8,50), w=0.05, prior="cauchy")
     postmed(c(-2,1,0,-4,8,50), w=0.2, prior="laplace", a=0.3)

