mixOnekn

`mixOnekn' is used for the estimation of the following two-component mixture model:
$$h(x;\boldsymbol{\theta},f) = \pi f(x-\mu_1)+(1-\pi)f(x-\mu_2),$$
where $\boldsymbol{\theta}=(\pi,\mu_1,\mu_2)^{\top}$ is the parameter to estimate,
$f$ is an unknown density function that is symmetric at zero.
The parameters are estimated by assuming $f$ is the normal density and the
first component has a mean of 0.
This function can be used to obtain initial values for the <code>mixMPHD</code> function.

Various functions are provided to estimate parametric mixture models
(with Gaussian, t, Laplace, log-concave distributions, etc.) and
non-parametric mixture models. The package performs hypothesis tests
and addresses label switching issues in mixture models.
The package also allows for parameter estimation in mixture of regressions,
proportion-varying mixture of regressions, and robust mixture of regressions.

Suyeon Kang

MixSemiRob

Mixture Models: Parametric, Semiparametric, and Robust

Xin Shen

Weixin Yao

Sijia Xiang

Yan Ge

mixOnekn function

<dl><dt>x</dt>
<dd>a vector of observations.</dd>
<dt>sigma.known</dt>
<dd>standard deviation of the first component (if known). Default is NULL,
which calculates the component standard deviations using the given observations <code>x</code>.</dd></dl>

Arguments

Two-component Normal Mixture Estimation with One Known Component — mixOnekn

<dl>

<dt>x</dt>
<dd>a vector of observations.</dd>


<dt>sigma.known</dt>
<dd>standard deviation of the first component (if known). Default is NULL,
which calculates the component standard deviations using the given observations <code>x</code>.</dd>

</dl>

mixOnekn: Two-component Normal Mixture Estimation with One Known Component

Description

Usage

Value

Arguments

See Also

Examples