wmonfromx            package:EbayesThresh            R Documentation

_F_i_n_d _m_o_n_o_t_o_n_e _E_m_p_i_r_i_c_a_l _B_a_y_e_s _w_e_i_g_h_t_s _f_r_o_m _d_a_t_a

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

     Given a vector of data, find the marginal maximum likelihood
     choice of  weight sequence subject to the constraints that the 
     weights are monotone decreasing.

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

     wmonfromx(xd, prior = "laplace", a = 0.5, tol = 1e-08, maxits = 20)

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

      xd: a vector of data

   prior: specification of the prior to be used; can be 'cauchy' or 
          'laplace'

       a: scale parameter in prior if 'prior="laplace"'.  Ignored if
          'prior="cauchy"'

     tol: absolute tolerance to within which estimates are calculated

  maxits: maximum number of weighted least squares iterations within
          the calculation

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

     The weights is found by marginal maximum likelihood.  The search
     is over weights corresponding to thresholds in the range  [0,
     sqrt{2 log n}], where n is the length of the data vector.

     An iterated least squares monotone regression algorithm  is used
     to maximize the log likelihood.  The weighted least squares
     monotone regression routine  'isotone' is used.

     To turn the weights into thresholds, use the routine 'tfromw';  to
     process the data with these thresholds,  use the routine
     'threshld'.

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

     The vector of estimated weights is returned

_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:

     'wfromx', 'isotone'

