gpdbiv                 package:evir                 R Documentation

_I_m_p_l_e_m_e_n_t_s _B_i_v_a_r_i_a_t_e _P_O_T _M_e_t_h_o_d

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

     Returns an object of class '"gpdbiv"' representing the fit of a
     bivariate POT (peaks over thresholds) model for joint excesses
     over thresholds.

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

     gpdbiv(data1 = NA, data2 = NA, u1 = NA, u2 = NA, ne1 = NA, ne2 = NA,
         global = FALSE, method = "BFGS", ...)

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

   data1: first data vector

   data2: second data vector

      u1: threshold for 'data1' (either this or 'ne1' must be given but
          not both)

      u2: threshold for 'data2' (either this or 'ne2' must be given but
          not both)

     ne1: number of upper extremes to be used for 'data1' (either this
          or 'u1' must be given but not both)

     ne2: number of upper extremes to be used for 'data2' (either this
          or 'u2' must be given but not both)

  global: should a global maximisation of the likelihood with respect
          to marginal and dependence parameters be undertaken. The
          default alternative is a two-stage local fit where first the
          marginal parameters are estimated and then the dependence
          parameter. This is much faster than a global fit.

  method: the optimization method (see 'optim'). The argument has been
          created (as distinct from ...) in order to make the '"BFGS"'
          method the default, as the default used by 'optim' is not
          recommended for the one-dimensional optimizations that occur
          when 'global = FALSE'.

     ...: other arguments passed to 'optim'

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

     This function implements a model suggested by Richard Smith (see
     references below). The marginal excess distributions are GPD
     distributions, as suggested by univariate EVT and implemented in
     gpd.   The dependence specification is known as the logistic or
     Gumbel dependence structure, but it would be easy to program
     alternatives.

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

     An object of class '"gpdbiv"' representing the fit and including
     parameter estimates and standard errors.

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

     Multivariate Threshold Methods, Richard L. Smith, in _Extreme
     Value Theory and Applications_, ed. J. Galambos, published by
     Kluwer, pages 225-248, 1994.

     Markov Chain Models for Threshold Exceedances, R.L. Smith, J.A.
     Tawn, S.G. Coles, _Biometrika_ *84*, 249-268, 1997.

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

     'gpd', 'plot.gpdbiv',  'interpret.gpdbiv'

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

     data(bmw) ; data(siemens)
     out <- gpdbiv(-bmw, -siemens, ne1 = 100, ne2 = 100)
     interpret.gpdbiv(out, 0.05, 0.05)
     ## Not run: plot(out) 

