Description Usage Arguments Details Value Author(s) References See Also Examples
This function provides the inferential algorithm to estimate a mixture of gamma distributions in which the mixing occurs over the shape parameter. It implements the collapsing approach for the GSM model, as discussed in Venturini et al. (2008).
1 2 |
y |
vector of data. |
J |
number of mixture components. |
G |
number of points where to evaluate the GSM density. |
M |
number of MCMC runs. |
a |
hyperparameter of the rate parameter prior distribution. |
b |
hyperparameter of the rate parameter prior distribution. |
alpha |
hyperparameter of the mixture's weights prior distribution. |
init |
initialization values. |
Suggestions on how to choose J
, a
and b
are provided in Venturini et al. (2008). In that work the alpha
vector is always set at (1/J
,...,1/J
), but here one is free to choose the value of the generic element of alpha
.
estim.gsm
returns an object of class "gsm", which is a list with the following components:
fdens |
matrix containing the posterior draws for the mixture's density. |
theta |
vector containing the posterior draws for the mixture's rate parameter. |
weight |
matrix containing the posterior draws for the mixture's weights. |
label |
matrix containing the posterior draws for the mixture's labels. |
data |
vector of data. |
Sergio Venturini sergio.venturini@unibocconi.it
Venturini, S., Dominici, F. and Parmigiani, G. (2008), "Gamma shape mixtures for heavy-tailed distributions". Annals of Applied Statistics, Volume 2, Number 2, 756–776. http://projecteuclid.org/euclid.aoas/1215118537
estim.gsm_theta
,
summary-methods
,
plot-methods
.
1 2 3 4 5 6 7 8 9 10 | ## Not run:
set.seed(2040)
y <- rgsm(500, c(.1, .3, .4, .2), 1)
burnin <- 100
mcmcsim <- 500
J <- 250
gsm.out <- estim.gsm(y, J, 300, burnin + mcmcsim, 6500, 340, 1/J)
summary(gsm.out, plot = TRUE, start = (burnin + 1))
plot(gsm.out, ndens = 0, nbin = 20, histogram = TRUE, start = (burnin + 1))
## End(Not run)
|
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