CENoisyFit {FitDynMix} | R Documentation |
This function estimates a dynamic mixture by means of the noisy Cross-Entropy method. Currently only implemented for the lognormal - generalized Pareto case, with Cauchy or exponential weight. This is mainly an auxiliary function, not suitable for the final user. Use CeNoisyFitBoot instead.
CENoisyFit(
x,
rawdata,
rho,
maxiter,
alpha,
nsim,
nrepsInt,
xiInst,
betaInst,
eps,
r = 5,
weight
)
x |
list: sequence of integers 1,...,K, where K is the mumber of datasets. Set x = 1 in case of a single dataset. |
rawdata |
either a list of vectors or a vector: in the former case, each vector contains a dataset to be used for estimation. |
rho |
real in (0,1): parameter determining the quantile of the log-likelihood values to be used at each iteration. |
maxiter |
non-negative integer: maximum number of iterations. |
alpha |
real in (0,1): smoothing parameter. |
nsim |
non-negative integer: number of replications used in the normal and lognormal updating. |
nrepsInt |
non-negative integer: number of replications used in the Monte Carlo estimate of the normalizing constant. |
xiInst |
non-negative real: shape parameter of the instrumental GPD. |
betaInst |
non-negative real: scale parameter of the instrumental GPD. |
eps |
non-negative real: tolerance for the stopping criterion of the noisy Cross-Entropy method. |
r |
positive integer: length of window to be used in the stopping criterion. |
weight |
'cau' or 'exp': name of weight distribution. |
See Rubinstein and Kroese (2004, chap. 6).
For each dataset, a list with the following elements is returned:
V (nreps x 12) matrix: updated mean and variance of the distributions used in the stochastic program. nit (positive integer): number of iterations needed for convergence. loglik (scalar): maximized log-likelihood.
Rubinstein RY, Kroese DP (2004). The Cross-Entropy Method. Springer.
CENoisyFitBoot
maxiter = 10
alpha = .5
rho = .05
eps = 1e-2
nsim = 1000
nrepsInt = 1000
xiInst = 3
betaInst = 3
r = 5
res <- CENoisyFit(1,Metro2019,rho,maxiter,alpha,nsim,nrepsInt,xiInst,betaInst,eps,r,'exp')