mixMPHD {MixSemiRob} | R Documentation |
Semiparametric Mixture Model by Minimizing Profile Hellinger Distance
Description
‘mixMPHD’ provides an efficient and robust estimation of a mixture of unknown location-shifted symmetric distributions using a semiparamatric method (Wu et al., 2017). As of version 1.1.0, 'mixMPHD' supports a two-component model, which is defined as
where is the parameter to estimate,
is an unknown density function that is symmetric at zero.
The parameters are estimated by minimizing the profile Hellinger distance (MPHD)
between the parametric model and a non-parametric density estimate.
Usage
mixMPHD(x, sigma.known = NULL, ini = NULL)
Arguments
x |
a vector of observations. |
sigma.known |
standard deviation of one component (if known). Default is NULL. |
ini |
initial values for the parameters. Default is NULL, which obtains the initial values
using the |
Value
A list containing the following elements:
lik |
final likelihood. |
pi |
estimated mixing proportion. |
sigma |
estimated component standard deviation. Only returned when |
mu |
estimated component mean. |
run |
total number of iterations after convergence. |
References
Wu, J., Yao, W., and Xiang, S. (2017). Computation of an efficient and robust estimator in a semiparametric mixture model. Journal of Statistical Computation and Simulation, 87(11), 2128-2137.
See Also
mixOnekn
for initial value calculation.
Examples
# Model: X ~ 0.3*N(0, 1) + 0.7*N(3, 1)
set.seed(4)
n = 100
p = 0.3
n1 = rbinom(1, n, p)
sigma1 = 1
sigma2 = 1
x1 = rnorm(n1, mean = 0, sd = sigma1)
x2 = rnorm(n - n1, mean = 3, sd = sigma2)
x = c(x1, x2)
ini = mixOnekn(x, sigma1)
mixMPHDest = mixMPHD(x, sigma1, ini = ini)