nmise                   package:sm                   R Documentation

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

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

     This function evaluates the mean integrated squared error of a
     density estimate which is constructed from data which follow a
     normal distribution.

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

     nmise(sd, n, h)

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

      sd: the standard deviation of the normal distribution from which
          the data arise. 

       n: the sample size of the data. 

       h: the smoothing parameter used to construct the density
          estimate. 

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

     see Section 2.4 of the reference below.

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

     the mean integrated squared error of the density estimate.

_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:

     `nise'

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

     x  <- rnorm(50)
     sd <- sqrt(var(x))
     n  <- length(x)
     h  <- seq(0.1, 2, length=32)
     plot(h, nmise(sd, n, h), type = "l")

