nise                   package: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 standardised 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 standardised 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.

_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)

