| Type: | Package |
| Title: | EM Algorithm for Multivariate Skew-Normal Distribution with Overparametrization |
| Version: | 0.1.1 |
| Date: | 2020-03-02 |
| Author: | Toshihiro Abe <abetosh@ss.nanzan-u.ac.jp>, Hironori Fujisawa <fujisawa@ism.ac.jp>, Takayuki Kawashima <kawashima@c.titech.ac.jp> |
| Maintainer: | Takayuki Kawashima <kawashima@c.titech.ac.jp> |
| Imports: | mvtnorm |
| Description: | Efficient estimation of multivariate skew-normal distribution in closed form. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Encoding: | UTF-8 |
| LazyData: | true |
| Suggests: | sn |
| RoxygenNote: | 7.0.2 |
| NeedsCompilation: | no |
| Packaged: | 2020-03-02 13:13:36 UTC; takayukikawashima |
| Repository: | CRAN |
| Date/Publication: | 2020-03-16 12:30:02 UTC |
EM algorithm for multivariate skew normal distribution.
Description
EM algorithm in closed form.
Usage
snem(
x,
eps = 0.9,
iter.eps = 10^-6,
stop.rule = c("parameter", "log-likelihood")
)
Arguments
x |
A data matrix. Each row is an observation vector. |
eps |
Weight parameter with |
iter.eps |
Convergence threshold. Default is 10^-6. |
stop.rule |
|
Details
The parameter eps is a tuning parameter which ensures that an initial covariance matrix is positive semi-definite.
Value
Location parameter (mu), covariance matrix (omega), skewness parameter (delta), and another expression of skewness parameter (lambda).
References
Abe, T., Fujisawa, H., and Kawashima, T. (2019) EM algorithm using overparametrization for multivariate skew-normal distribution, in preparation.
Examples
library(sn)
data(ais, package="sn")
x <- ais[c("BMI")]
snem(x, stop.rule ="log-likelihood")