MARSSinnovationsboot {MARSS}

Bootstrapped Data using Stoffer and Wall's Algorithm

Description

Creates bootstrap data via sampling from the standardized innovations matrix. This is an internal function in the MARSS-package and is not exported. Users should access this with MARSSboot.

Usage

MARSSinnovationsboot(MLEobj, nboot = 1000, minIndx = 3)

Arguments

MLEobj

An object of class marssMLE. This object must have a $par element containing MLE parameter estimates from e.g. MARSSkem() or MARSS(). This algorithm may not be used if there are missing datapoints in the data.

nboot

Number of bootstraps to perform.

minIndx

Number of innovations to skip. Stoffer & Wall suggest not sampling from innovations 1-3.

Details

Stoffer and Wall (1991) present an algorithm for generating CIs via a non-parametric bootstrap for state-space models. The basic idea is that the Kalman filter can be used to generate estimates of the residuals of the model fit. These residuals are then standardized and resampled and used to generate bootstrapped data using the MARSS model and its maximum-likelihood parameter estimates. One of the limitations of the Stoffer and Wall algorithm is that it cannot be used when there are missing data, unless all data at time t are missing.

Value

A list containing the following components:

boot.states

Array (dim is m x tSteps x nboot) of simulated state processes.

boot.data

Array (dim is n x tSteps x nboot) of simulated data.

marss

marssMODEL object element of the marssMLE object (marssMLE$marss) in "marss" form.

nboot

Number of bootstraps performed.

m is the number state processes (x in the MARSS model) and n is the number of observation time series (y in the MARSS model).

Author(s)

Eli Holmes and Eric Ward, NOAA, Seattle, USA.

References

Stoffer, D. S., and K. D. Wall. 1991. Bootstrapping state-space models: Gaussian maximum likelihood estimation and the Kalman filter. Journal of the American Statistical Association 86:1024-1033.

See Also

stdInnov(), MARSSparamCIs(), MARSSboot()

Examples

dat <- t(kestrel)
dat <- dat[2:3, ]
fit <- MARSS(dat, model = list(U = "equal", Q = diag(.01, 2)))
boot.obj <- MARSSinnovationsboot(fit)