excursions (version 2.0.6)

simconf.mixture: Simultaneous confidence regions for Gaussian mixture models

Description

excursions.simconf is used for calculating simultaneous confidence regions for Gaussian mixture models. The distribution for the process $x$ is assumed to be $$latex$$ The function returns upper and lower bounds $a$ and $b$ such that $P(a

Usage

simconf.mixture(alpha,
                mu,
                Q,
                w,
                ind,
                n.iter=10000,
                vars,
                verbose=0,
                max.threads=0,
                seed=NULL,
                mix.samp = TRUE)

Arguments

alpha

Error probability for the region.

mu

A list with the k expectation vectors $latex$.

Q

A list with the k precision matrices $latex$.

w

A vector with the weights for each class in the mixture.

ind

Indices of the nodes that should be analyzed (optional)

n.iter

Number or iterations in the MC sampler that is used for approximating probabilities. The default value is 10000.

vars

A list with precomputed marginal variances for each class (optional).

verbose

Set to TRUE for verbose mode (optional)

max.threads

Decides the number of threads the program can use. Set to 0 for using the maximum number of threads allowed by the system (default).

seed

Random seed (optional)

mix.samp

If TRUE, do MC integration by directly sampling the mixture, otherwise use sequential integration.

Value

• A list:
• a
  The lower bound.
• b
  The upper bound.
• a.marginal
  The lower bound for pointwise confidence bands.
• b.marginal
  The upper bound for pointwise confidence bands.

References

Bolin et al. (2015) Statistical prediction of global sea level from global temperature, Statistica Sinica, Vol 25, pp 351-367.

Examples

n = 11
K = 3
mu <- Q <- list()
for(k in 1:K){
  mu[[k]] = k*0.1 + seq(-5, 5, length=n)
  Q[[k]] = Matrix(toeplitz(c(1, -0.1, rep(0, n-2))))
}

## calculate the confidence region
conf = simconf.mixture(0.05, mu, Q, w = rep(1/3,3), max.threads=2)

## Plot the region
plot(mu[[1]],type="l")
lines(mu[[2]])
lines(mu[[3]])
lines(conf$a, col=2)
lines(conf$b, col=2)