

nise(sm)                                     R Documentation

_i_n_t_e_g_r_a_t_e_d _s_q_u_a_r_e_d _e_r_r_o_r _b_e_t_w_e_e_n _a _d_e_n_s_i_t_y _e_s_t_i_m_a_t_e _a_n_d _a
_N_o_r_m_a_l _d_e_n_s_i_t_y

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

     This function evaluates the integrated squared error
     between a density estimate constructed from a standard-
     ised version of the univariate data `y' and a standard
     normal density function.

_U_s_a_g_e_:

     nise(y, hmult=1)

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

       y: a vector of data.

   hmult: a factor which can be used to multiply the normal
          optimal smoothing parameter before construction of
          the density estimate.

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

     the data `y' are first standardised to have sample mean
     0 and sample variance 1.  The integrated squared error
     between a density estimate constructed from these stan-
     dardised data and a standard normal distribution is
     then evaluated.

     see Section 2.5 of the reference below.

_V_a_l_u_e_:

     the integrated squared error.

_S_i_d_e _E_f_f_e_c_t_s_:

     none.

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

     Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing
     Techniques for Data Analysis: the Kernel Approach with
     S-Plus Illustrations.  Oxford University Press, Oxford.

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

     `nmise'

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

     x <- rnorm(100)
     nise(x)

